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Books in Artificial intelligence expert systems and knowledge based systems

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    • Digital Twins

      • 1st Edition
      • May 1, 2026
      • Bedir Tekinerdogan + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 5 7 3 5
      • eBook
        9 7 8 0 4 4 3 4 5 5 7 2 8
      Digital Twins: Core Principles and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. As digital twin technology evolves, its integration with various advanced digital solutions is becoming essential for achieving real-time insights and autonomous decision-making. Challenges include understanding the interoperability of these technologies, managing data complexity, ensuring security, and optimizing for low-latency environments. The authors demystify digital twin technology, providing a clear framework for understanding how to effectively implement and utilize digital twins. The book addresses common challenges such as data integration, security, scalability, and the alignment of digital twin models with actual physical processes. After presenting core concepts of digital twins for software engineering, the book progresses to a section on integration with advanced digital solutions such as AI, IoT, Cloud computing, Big Data Analytics, and Extended Reality (XR). Next, the authors provide readers with a thorough presentation of digital twins applications in a variety of settings and industry/research topics. Finally, the book concludes with a discussion of challenges and solutions, along with future trends in digital twins research and development.

    • Artificial Intelligence and Machine Learning for Safety-Critical Systems

      • 1st Edition
      • May 1, 2026
      • Rajiv Pandey + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 5 9 7 3
      • eBook
        9 7 8 0 4 4 3 3 6 5 9 8 0
      Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide serves as a vital reference for engineers and system designers seeking to integrate AI and ML techniques into safety-critical environments. The book is meticulously structured into nine sections, each focusing on core applications and challenges unique to these high-stakes systems. Readers are guided through strategies that optimize resources, minimize failures, and bolster both system and public safety. With its practical approach, the guide aims to bridge the gap between advanced AI solutions and the rigorous demands of safety-critical industries.The book also delves into diverse domains such as pattern recognition, image processing, edge computing, IoT, encryption, and hardware accelerators. Each application area is explored to reveal the unique hurdles and solutions in deploying ML models in safety-sensitive contexts. Finally, the authors also emphasize the importance of explainable AI, ensuring model outputs are transparent and trustworthy rather than opaque. To further strengthen confidence in these systems, the text discusses legal, certification, and regulatory aspects, equipping readers with the tools necessary to achieve compliance and public trust.

    • AI Platforms as Global Governance for the Health Ecosystem

      • 1st Edition
      • May 1, 2026
      • Dominique J. Monlezun
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 5 0 5 6
      • eBook
        9 7 8 0 4 4 3 4 5 5 0 6 3
      AI Platforms as Global Governance for the Health Ecosystem: The Future’s Global Hospital provides comprehensive and actionable approaches for readers to understand and optimize responsible AI as global governance for the healthcare ecosystem. Written from the first-hand perspective of a practicing physician-data scientist and AI ethicist, the book maps out how to develop successful governance for AI platforms. The book explores how AI platforms can transform hospitals and clinical practice by digitally unifying patients, providers, and payors, advancing healthcare for all. This book defines and explains the main hurdles and technical innovations in responsibly governing AI platforms for efficient, equitable, and sustainable global healthcare. It explores the history, science, politics, economics, ethics, policy, as well as the future of these AI platforms, and how governance efforts can work toward the common good.

    • Metaverse and AI in Healthcare

      • 1st Edition
      • May 1, 2026
      • Jyotir Moy Chatterjee + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 9 5 8 1
      • eBook
        9 7 8 0 4 4 3 4 4 9 5 9 8
      "Metaverse and AI in Healthcare: A Federated Learning Approach" addresses the transformative integration of artificial intelligence and metaverse technologies in healthcare. It fills a critical gap by exploring how federated learning enables secure, decentralized data sharing and personalized medicine in virtual health platforms, meeting urgent demands for privacy, interoperability, and innovation. The book is structured into four parts covering foundational AI and federated learning concepts, augmented reality and metaverse applications, legal and cybersecurity challenges, and emerging strategic trends. Contributors from academia and industry present chapters on predictive modeling, cybersecurity frameworks, AR fitness, legal perspectives, and AI-driven medical tourism, supported by case studies and technical explanations. This reference equips graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare with practical knowledge and forward-looking analysis. It empowers readers to navigate evolving digital health ecosystems, addressing data privacy, customized care, and global access challenges through federated learning and metaverse solutions.

    • Digital Transformation in Artificial Systems

      • 1st Edition
      • February 1, 2026
      • Mirko Farina + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 2 9 6 4 7
      • eBook
        9 7 8 0 4 4 3 3 2 9 6 5 4
      Digital Transformation in Artificial Systems: Engineering Requirements and Political, Economic, and Philosophical Challenges is a groundbreaking work that scrutinizes the engineering necessities, political ramifications, and ethical dilemmas associated with the digital transformation, particularly within Artificial Systems. This transformative concept, which involves leveraging technological advancements to redefine operations and processes, is explored through an interdisciplinary lens, incorporating insights from computer science, engineering, philosophy, economy, and sociology. The book advocates for an inclusive digital transformation that addresses the global digital divide and promotes cooperation in digitization, industrialization, and innovation.It emphasizes the importance of understanding ethical challenges and developing fair policies that enhance human flourishing, social harmony, and moral good. The interdisciplinary framework provided by the authors offers essential insights into the forthcoming AI revolution and its societal impact.

    • 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.

    • 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.

    • Cybersecurity Defensive Walls in Edge Computing

      • 1st Edition
      • September 25, 2025
      • Agbotiname Lucky Imoize + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 1 0 9 0
      • eBook
        9 7 8 0 4 4 3 3 4 1 1 0 6
      Cybersecurity Defensive Walls in Edge Computing dives into the creation of robust cybersecurity defenses for increasingly vulnerable edge devices. This book examines the unique security challenges of edge environments, including limited resources and potentially untrusted networks, providing fundamental concepts for real-time vulnerability detection and mitigation through novel system architectures, experimental frameworks, and AI/ML techniques. Researchers and industry professionals working in cybersecurity, edge computing, cloud computing, defensive technologies, and threat intelligence will find this to be a valuable resource that illuminates critical aspects of edge-based security to advance theoretical analysis, system design, and practical implementation of defensive walls. With a focus on fast-growing edge application scenarios, this book offers valuable insights into strengthening real-time security for the proliferation of interconnected edge devices.

    • Edge Artificial Intelligence

      • 1st Edition
      • July 7, 2025
      • Parikshit Narendra Mahalle + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 6 7 2 0 8
      • eBook
        9 7 8 0 4 4 3 2 6 7 2 1 5
      Edge Artificial Intelligence: Algorithms, Applications, Challenges and Ethical Issues introduces the essentials of Edge AI and machine learning. It delves into the architecture, algorithms, and applications of Edge AI, offering insights into regulation and governance. Real-world case studies and practical examples are included, providing readers with the knowledge and tools to harness the transformative power of Edge AI. This book also addresses the ethical considerations and regulatory aspects of deploying AI at the edge.In addition to offering a clear understanding of real-time decision-making, enhanced privacy, and efficient applications, this book empowers both technical and nontechnical readers by providing practical insights, case studies, and ethical considerations. It helps users implement and govern Edge AI in a responsible and effective manner.

    • Edge Intelligence in Cyber-Physical Systems

      • 1st Edition
      • May 15, 2025
      • Wei Yu
      • English
      • Paperback
        9 7 8 0 4 4 3 2 6 5 7 2 3
      • eBook
        9 7 8 0 4 4 3 2 6 5 7 3 0
      Edge Intelligence in Cyber-Physical Systems: Foundations and Applications provides a comprehensive overview of best practices for building edge intelligence into cyber-physical systems. This book covers the foundations and applications of synergizing machine learning at the edge of CPS, leveraging an edge computing infrastructure. Divided into four parts, the first section of the book reviews the foundations, principles, and representative application domains of CPS. The second part covers machine learning, edge computing, and their needs in CPS, defining edge intelligence and its principles, challenges, and research directions. The third part presents tutorials and foundational research works on realizing edge intelligence in representative CPS. The fourth part explores the problem space of threats and countermeasures in building edge intelligence into CPS. Researchers, graduate students and professionals in computer science, data science, and electrical engineering will find this to be a valuable resource on the principles and applications of edge intelligence in cyber-physical systems as well as the development of interdisciplinary techniques to advance the field.