Intersection Of Artificial Intelligence Research Articles

StoryForge: AI-Powered Narrative with Dynamic Imagery and Voice

StoryForge is an AI-powered narrative generation system that utilizes a combination of OpenAI’s GPT-3.5 Turbo model [1], Hugging Face’s Diffusion-based text-to-image gen- erative model Stable Diffusion XL (SDXL) [4], and Hugging Face’s Text-to-Speech Bark model [10] to create dynamic and engaging stories tailored to user-specified constraints. Users can input parameters such as genre, number of characters, character details, key events, mood, tone, point of view, and reader age to guide the narrative generation process. The system leverages the power of GPT-3.5 Turbo to generate the story text [1], SDXL to generate images for key events [4], and Bark to synthesize speech for the narrative [10], providing a comprehensive and immersive storytelling experience. A Streamlit-based user interface enables seamless interaction with the system, allowing users to input constraints, view generated text and images, and listen to the synthesized audio. It transforms user inputs into a multisen- sory narrative experience, marking a significant stride in the intersection of artificial intelligence and imaginative storytelling. StoryForge demonstrates the potential of AI to revolutionize storytelling, empowering users to create personalized and cap- tivating narratives with ease and embark on a journey where words, images, and voices converge to create truly one-of-a-kind tales. Index Terms—StoryForge, GPT-3.5 Turbo, Bark, Stable Dif- fusion XL, Streamlit, Hugging Face, Generative Model, Story- telling, Multisensory

Emotion Analysis and Expression Algorithm of Dance Action Based on Machine Learning

Emotion analysis and expression algorithms represent a pivotal frontier in the intersection of artificial intelligence and human-computer interaction. These algorithms aim to decode and understand human emotions from various modalities such as text, speech, facial expressions, and physiological signals. This paper introduces the Context-Based Rough Sugeno Fuzzy (CBRSF) model tailored for emotion analysis and expression algorithms in the context of dance actions. With machine learning techniques, the CBRSF model integrates contextual information, rough set theory, and Sugeno fuzzy logic to accurately analyze and express emotions conveyed through dance movements. the power of machine learning techniques, the CBRSF model integrates various components, including contextual information, rough set theory, and Sugeno fuzzy logic, to provide a comprehensive framework for emotion analysis and expression. One of the key strengths of the CBRSF model lies in its ability to incorporate contextual information surrounding dance movements. Emotions conveyed through dance are often influenced by factors such as choreographic context, music, and cultural background. By integrating contextual cues into the analysis process, the CBRSF model can better capture the nuanced emotional nuances embedded within dance performances. The CBRSF model utilizes rough set theory to handle uncertainty and imprecision inherent in emotion analysis. Dance movements can be inherently ambiguous, making it challenging to accurately categorize the associated emotions. Rough set theory provides a principled framework for managing this uncertainty, allowing the CBRSF model to make informed decisions even in situations where data may be incomplete or inconsistent. Through comprehensive experimentation and evaluation, our proposed model achieves an emotion recognition accuracy of 98% across diverse dance action datasets, surpassing existing methods by 10.2%. Moreover, the CBRSF model enables nuanced emotion expression by dynamically adjusting dance movements based on real-time emotional cues.

AI based Face Recognition Attendance System

The AI Based Face Recognition Attendance System represents an innovative solution at the intersection of artificial intelligence and attendance management. Leveraging Python open-source libraries such as OpenCV and NumPy, alongside machine learning techniques, the system aims to streamline the attendance tracking process. By employing face detection algorithms and feature extraction methods, the system identifies individuals, records their attendance, and updates the database in real-time. The system architecture incorporates components like a frontend web application, real-time prediction module, registration form module, reporting module, Redis database, Streamlit framework, Insightface library, and AWS deployment. Through continuous improvement and innovation, the system enhances user experience, accuracy, and efficiency in attendance management.

Impact of artificial intelligence on health information literacy: guidance for healthcare professionals

Purpose This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all. Design/methodology/approach This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL. Findings Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices. Practical implications To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research. Originality/value This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

Exploring the Use and Implications of AI in Sexual and Reproductive Health and Rights: Protocol for a Scoping Review.

Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people's well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use. This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms. The scoping review follows the standard methodology developed by Arksey and O'Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings. The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024. The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work. OSF Registries osf.io/ma4d9; https://osf.io/ma4d9. PRR1-10.2196/53888.

Advancements and turning point of artificial intelligence in ophthalmology: A comprehensive analysis of research trends and collaborative networks.

Artificial intelligence (AI) has emerged as a transformative force with great potential in various fields, including healthcare. In recent years, AI has garnered significant attention due to its potential to revolutionise ophthalmology, leading to advancements in patient care such as disease detection, diagnosis, treatment and monitoring of disease progression. This study presents a comprehensive analysis of the research trends and collaborative networks at the intersection of AI and ophthalmology. In this study, we conducted an extensive search of the Web of Science Core Collection to identify articles related to 'artificial intelligence' in ophthalmology published from 1968 to 2023. We performed co-occurrence keywords and co-authorship network analyses using VOSviewer software to explore the relationships between keywords and country collaboration. We found a remarkable surge in articles applying AI in ophthalmology after 2017, marking a turning point in the integration of AI within the medical field. The primary application of AI shifted towards the diagnosis of ocular disease, which was particularly evident through keywords such as glaucoma, diabetic retinopathy and age-related macular degeneration. Analysis of the collaboration networks of countries revealed a global expansion of ophthalmology-related AI research. This study provides valuable insights into the evolving landscape of AI integration in ophthalmology, indicating its growing potential for enhancing disease detection, diagnosis, treatment planning and monitoring of disease progression. In order to translate AI technologies into clinical practice effectively, it is imperative to comprehend the evolving research trends and advancements at the intersection of AI and ophthalmology.

Revolutionizing the Legal sector: The Intersection of Artificial Intelligence and Law

Revolutionizing the Legal sector: The Intersection of Artificial Intelligence and Law

A task-oriented framework for generative AI in design

The intersection of Artificial Intelligence and Design disciplines such as Architecture, Urban Planning, Engineering and Product Design has been a longstanding pursuit, with Generative AI (GAI) ushering in a new era of possibilities. The research presented here explores how GAI can enhance creativity and assist Design practitioners with tasks to create products such as, but not limited to, renderings, concepts, construction techniques, materials, data analytics or maps. We apply a framework of combinational, exploratory and transformational creativity to organize how recent advancements in GAI can support each creative category. We propose a conceptual framework of GAI towards transformational creativity, and identify real-world examples to demonstrate GAI's impact, such as transforming sketches into detailed renders, facilitating real-time 3D model generation, predicting trends through analytics and creating images or reports via text prompts. Our work envisions a future where GAI becomes a real-time collaborator to complete certain automated tasks while liberating Designers to focus on transformational innovation.

IJCM_422A : Artificial Intelligence & Sustainable Development Goals in Health Care

Background: The intersection of Artificial Intelligence and Sustainable Development Goal (SDG) 3 presents both opportunities and challenges. AI has the potential to enhance healthcare delivery by improving diagnostic accuracy, enabling personalized treatment approaches, and facilitating remote healthcare services through telemedicine. AI-driven technologies can also streamline administrative tasks, optimize resource allocation, and support evidence-based decision-making in healthcare settings. Objective: This study seeks to identify challenges and opportunities for utilizing AI to advance global health objectives to support Sustainable Development Goal 3 Methodology: A review of existing literature was carried out to evaluate the current landscape of AI applications in healthcare and its potential implications for SDG achievement. We identified constraints such as data bias, ethical considerations, and accessibility barriers, and looked at the potential remedies and strategies for maximizing the positive impact of AI in healthcare. Conclusion: There exists significant hurdles into the integration of AI into healthcare however there are promising prospects for enhancing healthcare delivery, disease prevention, and drug discovery processes. By addressing issues such as data bias, ethical dilemmas, and accessibility challenges, AI holds the potential to revolutionize healthcare systems on a global scale. Embracing AI technologies can contribute significantly to realizing SDGs pertaining to health and well-being, thereby fostering more sustainable and equitable healthcare practices.

The role of AI-enhanced tools in overcoming socioeconomic barriers in education: A conceptual analysis

This conceptual analysis explores the transformative potential of AI-enhanced tools in addressing socioeconomic barriers within the educational landscape. By leveraging artificial intelligence (AI) technologies, the paper aims to examine how such tools can mitigate disparities arising from economic, social, and cultural factors. Through a critical analysis, it seeks to elucidate the role of AI in promoting equitable access, enhancing learning outcomes, and fostering inclusivity in education. The executive summary encapsulates the essence of the conceptual analysis. It provides a concise overview of the paper's objectives, methodology, expected outcomes, and implications. In recent years, the intersection of artificial intelligence (AI) and education has garnered significant attention as a potential solution to address persistent socioeconomic barriers within the educational landscape. The executive summary outlines the imperative to explore how AI-enhanced tools can serve as transformative agents in mitigating disparities arising from economic, social, and cultural factors. By leveraging AI technologies, educators and policymakers have the opportunity to revolutionize traditional educational practices and foster more inclusive learning environments. The summary highlights the urgent need to examine the role of AI in promoting equitable access, enhancing learning outcomes, and fostering inclusivity across diverse socioeconomic backgrounds. Through a critical analysis of existing literature, case studies, and empirical research, the conceptual analysis seeks to elucidate the potential of AI to bridge the digital divide and advance educational equity. It emphasizes the importance of identifying actionable strategies and best practices for leveraging AI technology to address systemic inequalities in education.

Artificial Intelligence and Machine Learning in Legal Research: A Comprehensive Analysis

This paper conducts a thorough investigation into the profound ramifications of Artificial Intelligence (AI) and Machine Learning (ML) on legal proceedings and the justice system. It assesses how these technologies are reshaping existing legal structures and principles by fundamentally altering judicial practices. Looking ahead, the paper anticipates a promising trajectory for AI and ML in legal research, elucidating their developmental pathways and introducing novel areas of inquiry. It emphasizes the necessity of an interdisciplinary approach, advocating for collaboration between legal professionals, data scientists, and ethicists to effectively address the ethical and practical challenges inherent in the integration of AI with the law. This synergy is deemed essential for navigating the complexities of ethics and practice that arise from the intersection of AI and the legal domain. Moreover, the research underscores the pivotal roles played by AI, ML, and related technologies, asserting that they transcend mere instrumental functions to become catalysts for comprehensive transformations in legal research methodologies. By embracing these technologies, the legal field stands on the brink of a substantial transformation, heralding the end of traditional practices and the dawn of a new era characterized by digital-age legal research paradigms.

Intersection of Artificial Intelligence and Medical Education (Student Abstract)

Can advanced AI-driven technologies transform the traditionally arduous educational process in medicine? This study takes a deep dive into how the publicly available OpenAI ChatGPT-3.5 performs in answering board-style questions designed for physicians training to become pathologists. Correctly answering 75% of 543 questions using an engaging and fast-paced format was an impressive performance. It underscores the potential as well as improvement opportunities of using interactive AI in future medical training.

In search of artificial intelligence (AI) literacy in teacher education: A scoping review

Artificial intelligence (AI) literacy has recently emerged on the educational agenda raising expectations on teachers’ and teacher educators’ professional knowledge. This scoping review examines how the scientific literature conceptualises AI literacy in relation to teachers’ different forms of professional knowledge relevant for Teacher Education (TE). The search strategy included papers and proceedings from 2000 to 2023 related to AI literacy and TE as well as the intersection of AI and teaching. Thirty-four papers were included in the analysis. The Aristotelian concepts episteme (theoretical-scientific knowledge), techne (practical-productive knowledge), and phronesis (professional judgement) were used as a lens to capture implicit and explicit dimensions of teachers’ professional knowledge. Results indicate that AI literacy is a globally emerging research topic in education but almost absent in the context of TE. The literature covers many different topics and draws on different methodological approaches. Computer science and exploratory teaching approaches influence the type of epistemic, practical, and ethical knowledge. Currently, teachers’ professional knowledge is not broadly addressed or captured in the research. Questions of ethics are predominantly addressed as a matter of understanding technical configurations of data-driven AI technologies. Teachers’ practical knowledge tends to translate into the adoption of digital resources for teaching about AI or the integration of AI EdTech into teaching. By identifying several research gaps, particularly concerning teachers' practical and ethical knowledge, this paper adds to a more comprehensive understanding of AI literacy in teaching and can contribute to a more well-informed AI literacy education in TE as well as laying the ground for future research related to teachers’ professional knowledge.

The intersection of Artificial Intelligence and cybersecurity: Challenges and opportunities

The fusion of artificial intelligence (AI) with cybersecurity represents a paradigm shift in our efforts to safeguard digital assets against a dynamic threat landscape. This manuscript comprehensively analyses AI's transformative role in cybersecurity, covering foundational principles, advanced methodologies, and ethical considerations. This article begins with exploring fundamental AI techniques such as machine learning and natural language processing. The manuscript delineates their applications in bolstering threat detection, vulnerability analysis, and incident response. Traditional approaches to vulnerability analysis are juxtaposed with AI-driven methodologies, highlighting the efficacy of automated scanning, threat prioritization, and adaptive risk assessment. Moreover, the manuscript delves into the pivotal role of AI-driven automation in expediting incident response, minimizing human error, and fortifying overall security postures. Ethical and privacy concerns surrounding AI deployment in cybersecurity are carefully examined, emphasizing the importance of responsible decision-making, privacy protection, and transparency. Looking ahead, emerging trends such as adversarial machine learning and zero trust security present promising avenues for further exploration, offering opportunities to enhance digital resilience against evolving threats.

AI in Test Automation: Overcoming Challenges, Embracing Imperatives

This scholarly article delves into the intersection of Artificial Intelligence (AI) and Test Automation, thoroughly examining the challenges inherent in implementing AI methodologies and elucidating imperatives critical for successful integration within contemporary software testing frameworks. The research entails a comprehensive exploration of challenges, ranging from intricacies in data quality to algorithmic biases, tool complexities, and integration challenges, drawing on empirical evidence from case studies and real-world scenarios. The paper articulates imperatives essential for overcoming challenges and ensuring the efficacy of AI in test automation. It emphasizes the significance of structured training programs, meticulous data management strategies, and the cultivation of an organizational culture conducive to the seamless integration of AI technologies. Through a rigorous analysis of successful case studies, the article provides a scholarly basis for the formulation of strategies and solutions to surmount challenges faced by organizations adopting AI in testing practices. A visual matrix aligning challenges with corresponding imperatives adds scholarly rigor to the article, offering a comprehensive framework for understanding the intricate relationships between challenges and the imperative strategies required for resolution. Furthermore, the exploration of emerging trends and innovations anticipates the future trajectory of AI-driven test automation, contributing valuable insights for strategic planning in the realm of software testing. This scholarly work underscores the importance of a systematic and informed approach to AI in Test Automation. By addressing challenges with academic rigor and embracing imperative strategies grounded in empirical evidence, organizations can position themselves at the forefront of AIdriven testing practices, advancing the field with a scholarly foundation for continued exploration and innovation.

Reviewing the impact of AI on renewable energy efficiency and management

In recent years, the intersection of artificial intelligence (AI) and renewable energy has emerged as a pivotal domain with transformative potential. This review delves into a comprehensive review of the impact that AI technologies have had on enhancing efficiency and management in the renewable energy sector. The integration of AI into renewable energy systems has ushered in a new era of optimization, addressing challenges and unlocking opportunities for sustainable energy production. AI's role in renewable energy begins with its ability to analyze vast datasets generated by energy systems, weather patterns, and consumption trends. Machine learning algorithms have been employed to predict energy demand, optimize grid operations, and enhance forecasting accuracy, thereby contributing to the increased efficiency of renewable energy sources. This application is crucial for overcoming the intermittent nature of renewable sources such as solar and wind power. Moreover, AI-driven technologies facilitate the intelligent management of energy storage systems, enabling better utilization of excess energy during peak production periods. Advanced control mechanisms, empowered by AI, have significantly improved the coordination of diverse energy sources, ensuring a seamless integration of renewable energy into existing power grids. Additionally, AI-driven predictive maintenance has proven instrumental in reducing downtime and optimizing the performance of renewable energy infrastructure. The review also explores the potential environmental and economic benefits of AI in renewable energy, emphasizing the role of smart grids, demand response systems, and decentralized energy production. As the world strives towards a sustainable energy future, this review offers valuable insights into the ongoing evolution of AI technologies and their impact on revolutionizing the efficiency and management of renewable energy systems. The findings presented here contribute to the growing body of knowledge aimed at accelerating the global transition to cleaner and more sustainable energy solutions.

Tourism demand forecasting using graph neural network

ABSTRACT Forecasting tourism demand is pivotal for crafting policies that nurture sustainable tourism. Despite its significance, the field grapples with challenges that limit its wider application, particularly in multi-step forecasting among diverse tourist attractions across scenarios. The absence of customised approaches in this field sparked our initiative to develop a cutting-edge graph neural network, specifically designed to address these needs. At the heart of the proposed algorithm lies a belief: graph neural network’s capability to analyze spatiotemporal correlations, fused with a deep learning architecture, makes it uniquely equipped to address the complexities of demand forecasting. This study develops three modules: dual correlation matrix, multi-head coordinate attention, and automatic control. These are meticulously crafted and synergized to tackle the methodological challenges prevalent in tourism demand forecasting. The experimental findings demonstrate proposed forecasting algorithm surpasses existing state-of-the-art algorithms in trials involving data from three renowned Chinese tourist cities. Through the validity of the algorithm, the conclusion supports the policy implication in developing multi-dimensional sustainable tourism, integrating the insights into future demand trends. This study not only advances the theoretical understanding of sustainable tourism and demand forecasting but also marks a significant stride in the intersection of artificial intelligence and tourism management.

The role of artificial intelligence in corporate digital strategies: evidence from China

PurposeIn the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.Design/methodology/approachTo explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.FindingsThe investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.Originality/valueThis study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

Securing Tomorrow: The Intersection of AI, Data, and Analytics in Fraud Prevention

Aim: This research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention. The advantages of integrating machine learning and data analytics into artificial intelligence systems for industry-wide fraud detection and prevention are examined in this study. Study Design: My approach involved conducting an extensive examination of existing literature and analysing numerous case studies to gather information on the role of artificial intelligence, data, and analytics in fraud prevention. Place and Duration of the Study: A broad spectrum of academic, corporate, and governmental sources is utilised to supply the research study with its international scope. This study examines publications and developments from 2019 to 2023. Methodology: The research procedure incorporated an exhaustive literature review. This assessment was composed of academic journals, conference proceedings, and official publications. A qualitative analysis was conducted to assess the data, identify commonalities, and evaluate the strengths and weaknesses of AI fraud protection solutions. A more comprehensive examination of practical implementations was facilitated by case studies, which enhanced comprehension of fraud prevention strategies propelled by AI. Results: The research revealed important findings concerning the various ways in which analytics, data, and artificial intelligence can be implemented to prevent fraudulent activities. An examination of comparisons between generative AI for social engineering, credit card analytics, and cyber-physical security for Internet of Things (IoT) networks illuminated the merits and demerits of different Artificial Intelligence (AI) approaches. Conclusion: According to the findings of the study, AI, data, and analytics may alter system defences against fraud. The above-mentioned results underscore the significance of flexible fraud prevention strategies. Constant collaboration, innovative technology, and ongoing investigation are required to remain ahead of evolving fraud techniques. The paper concludes by emphasising the significance of future challenges and orientations.

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: “Depth of anesthesia", “Control of anesthesia delivery", “Control of mechanical ventilation and weaning", “Event prediction", “Ultrasound guidance", “Pain management", “Operating room logistic", “Monitoring", and “Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.