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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.

    • 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.

    • Digital Supply Chain Transformation

      • 1st Edition
      • April 1, 2026
      • Vinaytosh Mishra
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 9 9 5 0
      • eBook
        9 7 8 0 4 4 3 3 3 9 9 6 7
      Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions delves into the intricate world of supply chain management, emphasizing the role of digital transformation in modern supply chains. Through a blend of theoretical learning and practical applications, readers will gain a deep understanding of foundational supply chain principles while exploring emerging trends and technologies reshaping the industry. Topics such as system dynamics modelling, machine learning, artificial intelligence, and end-to-end visibility will be explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management. There is a growing recognition that supply chain management can be significantly improved by leveraging modern technologies, such as machine learning and generative AI, to enhance efficiency and accuracy. Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions helps readers:Comprehend core principles and elements of supply chain management and its pivotal role in businesses and industries.Recognize the significance of digital transformation in supply chains, understanding the tools, technologies, and strategies essential for a successful transformation.Evalu... the importance of end-to-end supply chain visibility and employ methods and technologies to enhance this visibility in practical scenarios.Apply system dynamics modelling techniques to address complex supply chain problems and optimize supply chain processes.Utilize supply chain analytics and tools to make data-driven decisions, enhancing efficiency and reducing operational costs.Understand the potential of machine learning and artificial intelligence in supply chains, applying these technologies to innovate and solve real-world supply chain challenges.Identify and analyze emerging trends in supply chain management, anticipating future challenges and opportunities in the field.Demonstrate the ability to apply theoretical knowledge to practical scenarios, devising solutions for real-world supply chain challenges.Criticall... evaluate supply chain strategies, technologies, and solutions, recommending improvements and innovations based on informed analysis.

    • 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.

    • 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.

    • Multilevel Quantum Metaheuristics

      • 1st Edition
      • January 1, 2026
      • Siddhartha Bhattacharyya + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 1 3 6 7
      • eBook
        9 7 8 0 4 4 3 3 3 1 3 7 4
      Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.

    • Artificial Intelligence in Brain Disorders

      • 1st Edition
      • April 1, 2026
      • Pranav Kumar Prabhakar + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 7 2 2 1
      • eBook
        9 7 8 0 4 4 3 2 7 7 2 3 8
      Artificial Intelligence in Brain Disorders: Innovations in Diagnosis and Treatment focuses on the utilization of AI and machine learning to enhance current practices in the diagnosis and treatment of neurological disorders. Each chapter provides in-depth exploration of specific areas where AI can improve existing methodologies, offering practical guidance, case studies, and research findings that can be directly applied in the field. This book offers a detailed overview of AI and machine learning techniques relevant to neurological research. The book explains the application of AI in diagnosing and treating major neurological illnesses, and showcases the potential of AI in predicting diseases such as epilepsy and neurodegenerative disorders.

    • Advanced Intelligence Methods for Data Science and Optimization

      • 1st Edition
      • April 1, 2026
      • Amir Hossein Gandomi + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 8 9 4 0 8
      • eBook
        9 7 8 0 4 4 3 2 8 9 4 1 5
      Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

    • Digital Business Transformation in Healthcare

      • 1st Edition
      • April 1, 2026
      • Michael Mutingi
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 4 0 8 5
      • eBook
        9 7 8 0 4 4 3 3 3 4 0 9 2
      Digital Business Transformation in Healthcare: Advances, Models, Strategies and Frameworks provides a comprehensive view of digital transformation, outlining challenges and barriers while emphasizing critical success factors for healthcare digital transformation. The book introduces healthcare readiness and maturity models, enabling healthcare systems to choose appropriate roadmaps for digital transformation. By utilizing these models, healthcare providers can select effective and sustainable strategies for their digital transformation journey, avoiding the negative impacts of fragmented technology adoption. It is designed for both professionals already immersed in the healthcare industry and those seeking advanced knowledge in digital business transformation within healthcare.

    • Cybersecurity for Healthcare Systems in the Internet of Medical Things Era

      • 1st Edition
      • March 2, 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.