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Books in Artificial intelligence general

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    • 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.

    • Connected Diagnoses

      • 1st Edition
      • March 1, 2026
      • Keshav Kaushik + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 8 2 9 9 4
      • eBook
        9 7 8 0 4 4 3 3 8 3 0 0 7
      Connected Diagnoses: IoT, Healthcare, and Digital Forensics investigates the complex intersection of IoT, healthcare, and digital forensics. This book explores the intricate relationships between these fields, with a focus on cybersecurity, patient data ethics, and challenges in IoT investigations. This book advances knowledge on leveraging IoT securely to enhance patient care and digital forensic analysis, providing significant insights from experts along with practical guidance for those operating at the crossroads of these critical disciplines. The book helps professionals grasp, adapt to, and capitalize on the interconnected nature of emerging technologies to ensure ethics, security, and safety. It is a comprehensive resource that benefits researchers and practitioners seeking to understand the convergence of medical technology, interconnected devices, and digital forensics.

    • Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

      • 1st Edition
      • March 1, 2026
      • Hanif Heidari + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 1 3 3 5
      • eBook
        9 7 8 0 4 4 3 3 4 1 3 4 2
      Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders discusses the latest developed methods in IoT and its applications in neurological disorders that emphasize end-user requirements. Intelligent IoT is used to explore the intersection between medicine, data science, biomedical engineering, and healthcare systems. A comprehensive overview of modelling and analyzing the requirements of people with neurological disorders is presented in this book. Signals and images of biological activity are collected and analyzed based on patient specifications to facilitate more accurate diagnosis and treatment. The book also discusses cutting-edge AI methods for IoT devices designed to treat neurological conditions.

    • AI and Data Science in Medical Research

      • 1st Edition
      • March 1, 2026
      • Olfa Boubaker
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 6 3 8 5
      • eBook
        9 7 8 0 4 4 3 2 7 6 3 9 2
      AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies.The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

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

      • 1st Edition
      • February 1, 2026
      • Sayooj Aby Jose + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 2 3 4 0
      • eBook
        9 7 8 0 4 4 3 3 3 2 3 5 7
      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
      • Paperback
        9 7 8 0 4 4 3 4 3 8 5 2 3
      • eBook
        9 7 8 0 4 4 3 4 3 8 5 3 0
      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.

    • 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.

    • Integrating AI in Psychological and Mental Health Care

      • 1st Edition
      • March 1, 2026
      • Sandeep Kautish + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 1 4 9 5 4
      • eBook
        9 7 8 0 4 4 3 4 1 4 9 6 1
      Integrating AI in Psychological and Mental Health Care: Techniques, Applications, and Ethical Considerations introduces key concepts and the historical evolution of AI, providing a foundation for understanding its applications in mental health. The content delves into various aspects of AI, including diagnostic tools, machine learning algorithms, and natural language processing, highlighting their roles in enhancing therapeutic outcomes and improving patient care. The discussion encompasses significant mental health conditions such as anxiety, depression, and severe psychological disorders, showcasing how AI technologies can assist in diagnosis, treatment planning, and monitoring. Ethical considerations and privacy issues are critically examined, ensuring a balanced perspective on the benefits and challenges associated with AI-driven interventions. Practical applications, such as virtual psychotherapists and AI-enhanced cognitive behavioral therapy, illustrate real-world implementations and their impact on patient care. Additionally, case studies provide insights into successful AI applications in mental health settings, enhancing understanding of the potential advantages and obstacles involved.

    • Harnessing Artificial Intelligence to Ensure Diverse Global Teams

      • 1st Edition
      • March 1, 2026
      • Harish Garg + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 1 3 5 9
      • eBook
        9 7 8 0 4 4 3 3 4 1 3 6 6
      Harnessing Artificial Intelligence to Ensure Diverse Global Teams explores new research and applications of AI which can be used to address the distinct challenges of diverse, distributed teams. Incorporating compelling case studies and strategic guidance, the book demonstrates how AI can be developed and applied within systems and programs to promote inclusion, break down barriers, and enhance collaboration in cross-cultural organizational settings. Providing case studies and examples, this book equips computer scientists and engineers with actionable strategies for integrating AI seamlessly into programs and applications designed for diverse global teams. With contributions from experts in AI and team dynamics, this book will benefit leaders and team members seeking to leverage AI for improved teamwork across geographical and cultural boundaries, providing an up-to-date resource for uniting, uplifting, and optimizing global collaborations through inclusive artificial intelligence.