Books in Artificial intelligence general

Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics

1st Edition
February 1, 2026
Sayooj Aby Jose + 2 more
English

Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics offers a comprehensive exploration of innovative methodologies at the intersection of mathematics, biology, and medicine. This book delves into advanced mathematical modeling, artificial intelligence, and computational intelligence, providing essential tools for understanding and managing complex disease dynamics. Covering a wide range of topics, including fractional-order modeling, optimal control strategies, and privacy-preserving technologies, it addresses critical challenges in public health and healthcare systems. With contributions from leading experts, this volume bridges theoretical advancements and practical applications, making it an invaluable resource for researchers, healthcare professionals, and academics seeking interdisciplinary solutions to global health issues.

Learning-Driven Game Theory for AI

1st Edition
February 1, 2026
Mehdi Salimi + 1 more
English

Learning-Driven Game Theory for AI: Concepts, Models, and Applications offers in-depth coverage of recent methodological and conceptual advancements in various disciplines of Dynamic Games, namely differential and discrete-time dynamic games, evolutionary games, repeated and stochastic games, and their applications in a variety of fields, such as computer science, biology, economics, and management science. In this book, the authors bridge the gap between traditional game theory and its modern applications in artificial intelligence (AI) and related technological fields. The dynamic nature of contemporary problems in robotics, cybersecurity, machine learning, and multi-agent systems requires game-theoretic solutions that go beyond classical methods. The book delves into the rapidly growing intersection of pursuit differential games and AI, focusing on how these advanced game-theoretic models can be applied to modern AI systems, making it an indispensable resource for both academics and professionals. The book also provides a variety of applications demonstrating the practical integration of AI and game theory across various disciplines, such as autonomous systems, federated learning, and distributed decision-making frameworks. The book also explores the use of game theory in reinforcement learning, swarm intelligence, multi-agent coordination, and cybersecurity. These are critical areas where AI and dynamic games converge. Each chapter covers a different facet of dynamic games, offering readers a comprehensive yet focused exploration of topics such as differential and discrete-time games, evolutionary dynamics, and repeated and stochastic games. The absence of static games ensures a concentrated focus on the dynamic, evolving problems that are most relevant today.

Federated Learning for the Metaverse

1st Edition
January 11, 2026
Noor Zaman Jhanjhi + 3 more
English
Paperback
9 7 8 0 4 4 3 3 3 8 9 3 9
eBook
9 7 8 0 4 4 3 3 3 8 9 4 6

Federated Learning for the Metaverse: Applications in Virtual Environments provides readers with insights into how federated learning, a decentralized machine learning paradigm, can be strategically applied to address critical aspects of the metaverse. The book covers a wide range of topics, including privacy-preserving personalization, security, collaboration, adaptive learning environments, real-time communication, decentralized governance, language understanding, immersive learning experiences, avatar customization, and dynamic scene rendering.

Cybersecurity for Healthcare Systems in the Internet of Medical Things Era

1st Edition
January 1, 2026
Janmenjoy Nayak + 2 more
English
Paperback
9 7 8 0 4 4 3 2 7 4 4 8 0
eBook
9 7 8 0 4 4 3 2 7 4 4 9 7

Cybersecurity for Healthcare Systems in the Internet of Medical Things Era is driven by the imperative to address the intricate convergence of healthcare, technology, and security. In response to the burgeoning challenges presented by the Internet of Medical Things (IoMT), this book is designed to be an indispensable resource for computer systems experts, healthcare staff, and executives. It goes beyond the surface, offering strategic insights and actionable strategies that encompass not only the IoMT landscape but also the intersection of artificial intelligence, signal processing, and cyber security. Cybersecurity for Healthcare Systems in the IoMT Era serves as a problem-solving compass for a diverse readership in the healthcare landscape. For healthcare professionals and IT leaders, the book untangles the complexities of integrating and securing Internet of Medical Things (IoMT) devices, offering a roadmap for understanding and navigating this rapidly evolving terrain. Biomedical engineers, burdened with strategic and everyday decisions, find in this book strategic insights and actionable strategies, empowering them to make informed choices amidst the ever-changing challenges posed by technology and cybersecurity threats. The authors present a comprehensive guide that not only elucidates the challenges and opportunities presented by IoMT but also explores how the synergy of AI and signal processing can elevate healthcare systems. This integration is crucial in deciphering the intricate nuances of medical data, enhancing diagnostics, and fortifying the security of interconnected healthcare networks.

Challenges and Applications of Generative Large Language Models

1st Edition
January 1, 2026
Anitha S. Pillai + 2 more
English
Paperback
9 7 8 0 4 4 3 3 3 5 9 2 1
eBook
9 7 8 0 4 4 3 3 3 5 9 3 8

Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

Artificial Intelligence for the Water-Energy-Food Nexus

1st Edition
December 1, 2025
Shahryar Jafarinejad + 1 more
English
Paperback
9 7 8 0 4 4 3 3 4 0 1 9 2
eBook
9 7 8 0 4 4 3 3 4 0 2 0 8

Artificial Intelligence for the Water–Energy–Food Nexus considers the interrelationships among water, energy, and food, which are key to sustainable development and the potential of artificial intelligence (AI) as a useful tool to facilitate research and development within and between these areas as well as the water–energy–food (WEF) nexus.The first chapter introduces the WEF nexus concepts, assessment/analysis methodologies and tools, challenges, trends, and future perspectives. The second chapter summarizes AI applications to the water sector/industry with a focus on the importance, general applications, and real-world applications, as well as the challenges and future perspectives. The third chapter focuses on the application of AI techniques to the water and wastewater treatment systems, including treatment processes, urban drinking water systems, and integrated urban drainage systems. The fourth chapter explores the application of AI techniques to the nonrenewable and renewable energy systems, load monitoring, load demand forecasting, smart grids, energy optimization and process control, energy storage systems, and case studies and real-world applications, as well as the challenges in AI application to the energy industry and future directions. The application of AI techniques in the food industry in the areas of organoleptic properties, nutrition, toxicology, and food chemistry; biotechnology processes and microbiology security; food processing and manufacturing; conservation and storage conditions; supply chains, markets, and distribution; and food industry environments (circular economy, water, and energy management), as well as the challenges and opportunities are discussed in the fifth chapter. The final chapter reviews/presents the application of AI techniques to address problems associated with the WEF nexus. This book can be of interest to all those who are looking to understand and utilize AI techniques in the context of water, energy, food, and the WEF nexus, including researchers, advanced students, faculty, engineers, R&D, industry professionals, and policymakers.

AI and Data Science in Healthcare 5.0

1st Edition
December 1, 2025
Olfa Boubaker
English
Paperback
9 7 8 0 4 4 3 3 6 5 5 6 0
eBook
9 7 8 0 4 4 3 3 6 5 5 7 7

AI and Data Science in Healthcare 5.0 delves into the innovative developments in Healthcare 5.0, focusing on smart medical robots, devices, and connected hospitals. It explores the role of robotics in modern healthcare, including advanced wearables enhanced by deep learning. The volume also addresses the integration of blockchain technology, IoT, and cloud computing in healthcare, emphasizing real-time applications in precision healthcare. The discussion extends to energy optimization in smart hospitals, federated learning for IoMT networks, and advanced patient monitoring systems.The volume concludes with an overview of the future opportunities and challenges in Healthcare 5.0.

Multimodal Learning Using Heterogeneous Data

1st Edition
November 1, 2025
Saeid Eslamian + 3 more
English
Paperback
9 7 8 0 4 4 3 2 7 5 2 8 9
eBook
9 7 8 0 4 4 3 2 7 5 2 9 6

Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction

1st Edition
October 20, 2025
Shubham Mahajan + 2 more
English
Paperback
9 7 8 0 4 4 3 4 1 6 6 0 6
eBook
9 7 8 0 4 4 3 4 1 6 6 1 3

Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction explores the transformative potential of intelligent systems in enhancing human well-being through advancements in neurocognitive technologies, robotics, and AI-driven healthcare. This book addresses the fragmented knowledge at the intersection of these fields, enabling readers to understand the latest advancements and discover practical strategies for implementation. It highlights the importance of ethical considerations and governance in deploying AI and robotics in healthcare settings. Each chapter covers various aspects, including personalized medicine, human-robot interaction, cognitive robotics, wearable technologies, and the role of AI in mental health. The book also discusses the implications of cloud computing, big data, and human-computer interaction in enhancing cognitive and emotional well-being.

Data-Driven Diagnostics and Disease Prediction with AI Optimization

1st Edition
October 1, 2025
Shailendra Pratap Singh + 3 more
English
Paperback
9 7 8 0 4 4 3 2 6 7 4 7 5
eBook
9 7 8 0 4 4 3 2 6 7 4 8 2

Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.

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