Artificial Intelligence and Machine Learning-Based Security Enforcement Techniques for 6G Communication

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

BackgroundArtificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.MethodsA cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.ResultsFemale participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.ConclusionThis study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector.

Membrane technologies play a vital role in sustainable development due to their efficiency in separation, purification, and chemical processing applications. However, the discovery and optimization of new membrane materials remain largely reliant on trial-and-error experimentation, limiting the pace of innovation. Artificial intelligence (AI) and machine learning (ML) are increasingly being applied to overcome these limitations by enabling data-driven insights, predictive modeling, and rapid material design. These computational approaches have shown significant promise in accelerating membrane fabrication, improving process simulation, detecting and mitigating fouling, and enhancing membrane characterization. This review provides a comprehensive overview of the recent advancements in the integration of AI and ML within membrane and material science. Fundamental AI and ML concepts relevant to membrane science are discussed, together with their applications in membrane fabrication, performance prediction, process modeling, fouling control, and membrane design. Challenges related to data quality, model interpretability, and the integration of domain-specific knowledge are also highlighted, along with potential future research directions. Compared with conventional empirical approaches, the advantages of AI and ML in handling complex, multivariate datasets and accelerating innovation are demonstrated. Overall, this review underscores the transformative potential of AI and ML in developing next-generation membranes with improved efficiency, selectivity, and sustainability across various industrial applications. Although several reviews have explored ML applications in membrane processes, comprehensive integration across material design, fabrication, fouling control, optimization, and process modeling remains limited.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.

Purpose: This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in brand management. It aims to understand how AI technologies can optimize brand awareness, enhance personalized communication, and improve brand equity measurement, thereby redefining brand management strategies in the digital era. Design/methodology/approach: A systematic literature review was conducted, analyzing research studies published between 2014 and 2024. The methodology focused on articles that delve into the integration of AI and ML within brand management contexts. This approach was chosen to consolidate and synthesize a broad spectrum of findings on AI’s impact on brand management practices. Findings: The review identified significant enhancements in brand management facilitated by AI, particularly in consumer engagement and customization of consumer interactions through data-driven insights. AI's capability to analyze big data has enabled more precise consumer segmentation and targeting, thereby influencing brand loyalty and overall brand equity positively. Research limitations/implications: The primary limitation of this study is its reliance on data from articles indexed only in Scopus, potentially omitting relevant studies published in other languages or databases. Future research should expand the scope to include these sources and explore empirical validations of the proposed theoretical impacts of AI on brand management. Practical implications: This research highlights the practical applications of AI in improving brand management strategies, offering insights into effective AI integration into marketing practices. Businesses are encouraged to adopt AI-driven tools for better market segmentation, consumer behavior predictions, and enhanced customer relationship management. Social implications: The findings could influence public attitudes towards brand interaction by fostering greater acceptance of AI in consumer relations. The research supports enhanced corporate social responsibility through AI's ability to provide more targeted and meaningful consumer interactions. Originality/value: This paper contributes to the academic and practical understanding of AI’s role in brand management by synthesizing existing research and identifying future research directions. It is valuable to academicians, marketing professionals, and policymakers interested in the implications of AI technology in brand strategies. Keywords: Brand Management, Artificial Intelligence, Machine Learning, Consumer Engagement, Marketing Strategies. Category of the paper: Literature review.

A significant fundamental change in healthcare can be seen by the integration of artificial intelligence (AI) and machine learning (ML) with medical imaging (MI). This integration holds the potential to improve patient care standards, change clinical procedures, and improve diagnostic accuracy. This chapter presents the possible impact, addressed challenges, opportunities, and impacts on medical diagnosis of recent advancements and growing advances in the application of AI and ML in the field of MI. The rapid development of AI and ML technologies have pushed MI into a new phase of data-driven healthcare and personalized medicine. Furthermore, the applications of AI and ML in MI are a variety of including automated picture analysis and disease identification to prediction, revolutionizing pathology and radiology, and healthcare management. It also suggests a future in which these innovations will work together to improve patient care, improve diagnostic accuracy, and advance healthcare delivery.

This study examines the potential of digital transformation in Islamic education, focusing on the integration of artificial intelligence (AI) and machine learning (ML) to enhance personalized learning in Madrasah. Islamic educational institutions have traditionally followed structured curricula, but the evolving needs of 21st-century learners demand more adaptive and personalized approaches. AI and ML technologies offer significant opportunities to tailor educational experiences to individual student needs, facilitating improved engagement and learning outcomes. This paper reviews existing literature on the application of AI in education and explores how these technologies can be adapted to the specific context of Madrasah. Challenges, including technical infrastructure, teacher preparedness, and ethical considerations in AI implementation, are also addressed. The findings suggest that while AI integration presents substantial benefits, it requires careful planning and resource allocation to ensure successful implementation. This review contributes to a better understanding of how AI can transform Islamic education through personalized learning. Recommendations for future research are provided to guide the practical application of these technologies in religious education settings.

The integration of artificial intelligence (AI) and machine learning (ML) into the field of microbiology is making a significant impact on both research and clinical practices. These technologies are helping us better understand microbial systems, improve diagnostic accuracy, speed up drug discovery, and tailor treatments to individual patients. Some exciting applications of AI in microbiology include helping with genome sequencing, predicting antimicrobial resistance, developing vaccines, and analyzing microbiomes. In industrial settings, AI is transforming quality control and safety in the pharmaceutical, food, and cosmetic industries. However, despite these advancements, there are still challenges that prevent us from fully realizing the potential of AI and ML in microbiology. Issues such as inconsistent data quality, the complexity of algorithms that can be difficult to interpret, a lack of regulatory guidelines, and ethical concerns surrounding data privacy and algorithm bias must be addressed. There is a pressing need for collaboration across various fields, transparent development of models, and cooperation among academic institutions and healthcare providers globally. Looking to the future, we can expect innovations in deep learning, hybrid modeling techniques, and the development of international standards and policies to drive further advancements. With responsible implementation and ongoing research, AI and ML are set to become essential tools in microbiology, offering innovative solutions to persistent challenges in healthcare, diagnostics, and environmental monitoring. This review focuses on the current uses, challenges, and future directions of AI in microbiology, highlighting the importance of ethical considerations, collaboration, and data-driven strategies to achieve meaningful and sustainable outcomes.

Early warning score (EWS) have become an essential component of patient safety strategies in healthcare environments worldwide. These systems aim to identify patients at risk of clinical deterioration by evaluating vital signs and other physiological parameters, enabling timely intervention by rapid response teams. Despite proven benefits and widespread adoption, conventional EWS have limitations that may affect their ability to effectively detect and respond to patient deterioration. There is growing interest in integrating continuous multimodal monitoring technologies and advanced analytics, particularly artificial intelligence (AI) and machine learning (ML)-based approaches, to address these limitations and enhance EWS performance. This review provides a comprehensive overview of the current state and potential future directions of AI-based bio-signal monitoring in early warning system. It examines emerging trends and techniques in AI and ML for bio-signal analysis, exploring the possibilities and potential applications of various bio-signals such as electroencephalography, electrocardiography, electromyography in early warning system. However, significant challenges exist in developing and implementing AI-based bio-signal monitoring systems in early warning system, including data acquisition strategies, data quality and standardization, interpretability and explainability, validation and regulatory approval, integration into clinical workflows, and ethical and legal considerations. Addressing these challenges requires a multidisciplinary approach involving close collaboration between healthcare professionals, data scientists, engineers, and other stakeholders. Future research should focus on developing advanced data fusion techniques, personalized adaptive models, real-time and continuous monitoring, explainable and reliable AI, and regulatory and ethical frameworks. By addressing these challenges and opportunities, the integration of AI and bio-signals into early warning systems can enhance patient monitoring and clinical decision support, ultimately improving healthcare quality and safety. In conclusion, integrating AI and bio-signals into the early warning system represents a promising approach to improve patient care outcomes and support clinical decision-making. As research in this field continues to evolve, it is crucial to develop safe, effective, and ethically responsible solutions that can be seamlessly integrated into clinical practice, harnessing the power of innovative technology to enhance patient care and improve individual and population health and well-being.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in industrial automation and cybersecurity has become increasingly vital as industries transition towards smarter and more efficient systems. With the rapid advancement of technology, AI and ML are playing a key role in optimizing operations, enhancing safety, and improving overall system performance. This paper explores the applications of AI and ML in industrial automation, focusing on their transformative impact on manufacturing, process control, and predictive maintenance. Additionally, the paper highlights the critical role these technologies play in enhancing cybersecurity frameworks, particularly in the context of industrial control systems and critical infrastructure. The combination of AI and ML allows for real-time decision-making, anomaly detection, and the mitigation of security risks in an increasingly interconnected industrial environment. The findings suggest that AI and ML are not only optimizing operational efficiency but also providing advanced threat detection mechanisms to combat sophisticated cyber threats. This paper presents a detailed review of these technologies' capabilities and challenges, offering insights into their future potential in industrial settings.

This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML), exploring foundational concepts, emerging trends, and diverse applications. AI and ML have rapidly evolved, becoming pivotal in numerous fields including healthcare, finance, manufacturing, and autonomous systems. The review begins by outlining key concepts, including the distinctions between AI, ML, and deep learning, and delves into various learning paradigms such as supervised, unsupervised, and reinforcement learning. It highlights significant advancements, such as neural networks, natural language processing, and generative models, emphasizing their impact on industry and research. The paper also examines current trends, including the rise of ethical AI, explain ability, and the integration of AI with Internet of Things (IoT) and edge computing, which are shaping the future landscape of AI applications. Additionally, it addresses the challenges and limitations associated with AI and ML, such as data privacy concerns, model interpretability, and the need for sustainable computing solutions. By synthesizing insights from recent literature, this review provides a holistic understanding of the AI and ML domains, offering perspectives on future directions and innovations. this review aims to provide a holistic understanding of AI and ML, offering perspectives on future directions and innovations.

Artificial Intelligence (AI) and Machine Learning (ML) have become pivotal technologies driving innovation across multiple sectors, including education, e-commerce, cybersecurity, and misinformation detection. Despite their transformative potential, the integration of AI and ML into these fields presents significant challenges, including data privacy concerns, scalability issues, and ethical dilemmas. This review paper synthesizes the current state of research on AI and ML applications, highlighting both their successes and the problems they encounter. The paper focuses on how AI enhances personalization in education and e-commerce, strengthens security frameworks in the Internet of Things (IoT) ecosystem, and combats the spread of misinformation. By reviewing key studies, this paper identifies gaps in the existing literature and proposes directions for future research to address these challenges. The overarching goal is to provide a comprehensive understanding of how AI and ML are reshaping digital ecosystems and what is needed to ensure their ethical and effective implementation.

The rapid advancement of information technology has led to an upsurge in cybercriminal activities. As technology continues to evolve, so do the tactics used by individuals involved in digital offenses. Trends in complex, distributed, and internet[1]based computing have raised significant concerns regarding information security and privacy. Cyber infrastructures, in particular, are highly susceptible to intrusions and various threats. Traditional security measures like sensors and detectors are inadequate for safeguarding these infrastructures, necessitating the development of more sophisticated IT solutions capable of modeling normal behaviors and identifying anomalies. To address these challenges effectively, cyber defense systems must exhibit traits such as flexibility, adaptability, and robustness. They should be able to detect a wide range of threats while making intelligent real-time decisions. Given the sheer volume and speed of cyberattacks, relying solely on human intervention is inadequate for prompt analysis and response. Many of these attacks are orchestrated by intelligent agents like computer worms and viruses, making it essential to combat them using intelligent, semi-autonomous agents that can promptly detect, evaluate, and respond to cyber threats. These computer-generated forces must manage the entire process of responding to attacks, encompassing the identification of the attack type, its targets, the appropriate response, and the prioritization of secondary attack prevention. Furthermore, cyber intrusions are not confined to a single location; they represent a global menace to computer systems worldwide. The expansion of the internet has made knowledge and tools for cybercrime readily accessible to a wide audience, no longer limited to educated specialists. Traditional, rigid algorithms with hard-wired logic have proven ineffective in countering dynamically evolving cyberattacks. This underscores the importance of innovative approaches, particularly the application of Artificial Intelligence (AI), to enhance our capability to combat cybercrimes. AI introduces flexibility and learning capabilities to software, thereby assisting humans in the fight against cybercrimes. Various AI techniques, inspired by nature, including Computational Intelligence, Neural Networks, Intelligent Agents, Artificial Immune Systems, Machine Learning, Data Mining, Pattern Recognition, Fuzzy Logic, and Heuristics, are playing an increasingly crucial role in the detection and prevention of cybercrimes. AI empowers the design of autonomic computing solutions that can adapt to their usage context, employing methods such as self-management, self-tuning, self[1]configuration, self-diagnosis, and self[1]healing. In the realm of information security, AI represents a promising area of research with a focus on enhancing cybersecurity measures in cyberspace. The term "Artificial Intelligence" is used to describe a machine's ability to emulate human-like activities, including problem solving and learning, a concept often referred to as machine learning. The next generation of cybersecurity products is increasingly incorporating Artificial Intelligence and Machine Learning technologies. By analyzing extensive datasets of cybersecurity, network, and physical information, providers of cybersecurity solutions aim to identify and thwart abnormal behavior. Various approaches are employed to utilize AI for cybersecurity. Some applications analyze raw network data to detect irregularities, while others focus on user-entity behavior to identify deviations from the norm. The choice of approach depends on the type of data streams and the level of effort required by analysts