AI and Machine Learning Applications in Industrial Automation and Cybersecurity

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.

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.

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

Predictive maintenance is a critical task for ensuring the reliability and efficiency of industrial systems. The integration of artificial intelligence (AI) and mathematical models has shown great potential in improving the accuracy and efficiency of predictive maintenance. This study provides an overview of the different types of mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid models. The study also discusses how AI techniques, such as machine learning and deep learning, can be used to enhance the accuracy and efficiency of predictive maintenance models. Additionally, the article highlights some of the challenges and limitations of integrating AI and mathematical models for predictive maintenance in industrial systems. Finally, this study provides a case study to demonstrate the practical application of the integrated approach for predictive maintenance in an industrial setting. This article aims to provide a comprehensive overview of the state-of-the-art in integrating AI and mathematical models for predictive maintenance and to provide guidance for researchers and practitioners working in this field.

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 integration of Artificial Intelligence (AI) and Machine Learning (ML) in Quality Assurance (QA) automation is transforming software testing by enhancing accuracy, efficiency, and adaptability. AI-driven techniques, such as intelligent test case generation, self-healing automation scripts, and predictive analytics, enable faster defect detection and reduced maintenance efforts. ML models can analyze historical test data to optimize test execution, prioritize critical test scenarios, and identify patterns in software defects. Despite challenges such as data dependency, integration complexity, and the need for specialized expertise, AI and ML continue to drive innovation in QA automation. This paper explores the key applications, benefits, challenges, and future prospects of AI and ML in QA automation, highlighting their role in improving software reliability and development agility.

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.

The transformation of education in the digital era has been significantly accelerated by the integration of Artificial Intelligence (AI) and Machine Learning (ML), fundamentally reshaping how learning is designed, delivered, and assessed. This study aims to systematically identify emerging trends, key benefits, prevailing challenges, and future directions of AI and ML applications in education through a Systematic Literature Review (SLR) approach. The reviewed literature was sourced from leading academic databases, including Scopus, IEEE Xplore, and ScienceDirect, covering publications from 2015 to 2025. The findings reveal that AI and ML technologies have been widely implemented in various educational domains, particularly in adaptive learning systems, automated assessment mechanisms, and intelligent virtual assistants that facilitate personalized learning experiences. Despite these advancements, several critical challenges persist, notably digital inequality, data privacy concerns, and the limited technological literacy among educators, which hinder the effective adoption of these technologies. Furthermore, the study highlights that the future of education will increasingly rely on the integration of intelligent systems that enable data-driven, flexible, and learner-centered environments. The insights derived from this SLR are expected to provide valuable guidance for policymakers, educators, and technology developers in formulating adaptive and sustainable educational strategies in the era of artificial intelligence.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

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.

The integration of artificial intelligence (AI) into postharvest agricultural practices has advanced considerably in recent decades, driven by substantial progress in scientific research and technological development. Tomato softening and quality degradation during postharvest storage present major challenges in minimizing food waste and maintaining market value. This review explores the integration of artificial intelligence (AI) and machine learning (ML) technologies with non-destructive methods such as computer vision, imaging, electrical signal analysis, to monitor and predict tomato ripening stages and shelf life. Recent studies demonstrate high prediction accuracies using advanced models, including artificial neural networks, ensemble learning, and fuzzy inference systems, which analyze features like firmness, color, lycopene content, and texture. These AI-driven approaches enable accurate classification of ripeness stages and optimization of storage conditions, offering significant advantages over traditional destructive techniques. For the future ML, integration of large and complex algorithms and AI-driven systems controlling smart ripening chambers by adjusting ethylene concentration, humidity, and temperature based on real-time sensor feedback, will support uniform ripening, precision postharvest handling. The potential of mobile applications and/or with the advent of recently developed smart glasses integrated with artificial intelligence, producers will be able to assess fruit ripeness in real time simply by visually inspecting the fruit, enabling rapid and informed harvesting decisions. Overall, the adoption of AI-based solutions in tomato postharvest management holds promise for improving quality monitoring, reducing spoilage, and enhancing sustainability in the agri-food sector.

The integration of artificial intelligence (AI) and machine learning (ML) into crop management is transforming modern agriculture by enhancing efficiency, sustainability, and resilience. This review explores the multifaceted applications of AI and ML in key areas such as precision farming, pest and disease management, and harvest optimization. The use of AI-driven predictive analytics allows for more accurate forecasting of crop yields, pest outbreaks, and weather conditions, enabling farmers to make data-driven decisions that optimize resource use and reduce environmental impacts. A significant advancement is the integration of AI and ML with the Internet of Things (IoT) and autonomous farming equipment. These technologies enable real-time monitoring and precise interventions, enhancing productivity and minimizing labor costs. In crop breeding and genomics, AI accelerates the development of resilient crop varieties, which is crucial for adapting to climate change and increasing food demands. Despite the many benefits, challenges such as data quality, infrastructure limitations, and high implementation costs remain. The adoption of AI technologies is uneven, with small-scale farmers in developing regions facing barriers due to limited access to data and resources. Ethical concerns related to data privacy and the digital divide must also be addressed to ensure equitable access to these technologies. The future of AI and ML in agriculture lies in the development of more advanced predictive models, enhanced integration with the IoT, and the widespread use of autonomous farming systems.

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

The integration of artificial intelligence (AI) with seismic data analysis marks a significant advancement in reservoir characterization, promising enhanced accuracy, efficiency, and comprehensiveness in geological interpretations. This paper presents a conceptual model that combines machine learning algorithms with traditional seismic processing techniques, creating a hybrid approach for automated, high-precision analysis. The proposed model outlines a systematic workflow involving data preprocessing, feature extraction, application of machine learning, and integration into comprehensive geological models. Key findings emphasize the effectiveness of algorithms like convolutional neural networks in detecting geological features and predicting reservoir properties. The study also highlights the importance of high-quality data, robust validation techniques, and cross-disciplinary collaboration. Practical implications include improved exploration efficiency, optimized drilling strategies, and enhanced reservoir management. Recommendations for industry adoption focus on investing in data quality, computational infrastructure, training, and collaborative efforts. This integration offers a transformative approach to reservoir characterization, driving better decision-making and improved outcomes in exploration and production activities. Keywords: Artificial Intelligence, Seismic Data Analysis, Reservoir Characterization, Machine Learning, Geological Interpretation, Data Integration.

This chapter provides a comprehensive introduction to the foundational concepts of Artificial Intelligence (AI), Machine Learning (ML), and Big Data, emphasizing their interdependence and transformative impact across modern industries. The chapter begins by defining AI as the capability of machines to perform cognitive functions traditionally associated with human intelligence, and clarifies the relationship between AI and ML, highlighting ML as a critical subset focused on data-driven learning and prediction. It explores the core methodologies of ML, including supervised, unsupervised, and reinforcement learning, and discusses the challenges posed by the exponential growth of digital data, particularly the need for scalable data collection and labeling techniques. The chapter further examines the concept of Big Data, characterized by its volume, velocity, and variety, and its essential role in enabling advanced analytics and real-time decision-making. The integration of AI, ML, and Big Data is presented as a catalyst for innovation in sectors such as manufacturing, healthcare, and especially insurance. In the insurance industry, these technologies are revolutionizing operations through automated claims processing, fraud detection, personalized services, and predictive risk management. The chapter also addresses the challenges of integrating AI and Big Data, including data quality, cybersecurity, and ethical considerations, and underscores the competitive imperative for organizations to adopt these technologies.