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Books in Artificial intelligence expert systems and knowledge based systems

Computational Intelligence in Mechatronics
Solving Real-World Problems in Electronic Systems Design
1st Edition
September 15, 2026
Mohamed Arezki Mellal
English
Computational Intelligence in Mechatronics: Solving Real-World Problems in Electronic Systems Design provides a comprehensive exploration of the diverse applications of computational intelligence in the realm of applied electronics. By compiling cutting-edge research and practical case studies, the book bridges the gap between theory and practice, offering insights into how CI techniques can be effectively utilized to solve real-world problems in electronic systems design, analysis, and optimization. Through a combination of theoretical foundations, algorithmic implementations, and practical examples, readers will gain a deeper understanding of the potential benefits and limitations of CI in various applications within the field of applied electronics.From optimizing circuit designs to enhancing signal processing algorithms, CI has demonstrated its efficacy in addressing challenges across diverse domains such as telecommunications, consumer electronics, renewable energy systems, and medical devices. As the demand for intelligent electronic systems continues to grow, understanding and harnessing the potential of computational intelligence becomes imperative for researchers, engineers, and practitioners in the field of applied electronics.
Smart Healthcare 2.0
Integrating Digital Twins with AI-Driven Predictive Analytics
1st Edition
September 1, 2026
Ramesh Chandra Poonia + 1 more
English
Smart Healthcare 2.0: Integrating Digital Twins with AI-Driven Predictive Analytics offers a ground-breaking exploration of how digital twin technology, combined with real-time sensing and predictive analytics, is transforming healthcare delivery. As the global healthcare landscape shifts toward proactive, personalized care, this book addresses the urgent need for comprehensive resources that unify artificial intelligence, Internet of Things (IoT), and biomedical engineering within the digital twin framework. It provides an essential guide for researchers, engineers, and clinicians aiming to harness virtual patient models and data-driven insights to improve health outcomes and system efficiency in the era of ubiquitous healthcare.This volume covers a wide spectrum of topics, starting with foundational concepts of digital twins in precision health and advancing through smart sensing technologies, scalable system architectures, and AI-powered predictive analytics. Readers will explore detailed discussions on edge-cloud computing, secure communication protocols including blockchain, and simulation platforms that enable virtual patient modeling. The book also addresses critical themes such as chronic disease management, emergency response optimization, ethical AI deployment, interoperability standards, and workforce readiness. Real-world case studies and future-focused chapters on cognitive twins and quantum simulation provide a rich, multidisciplinary perspective. Each chapter is complemented by pedagogical tools and supported by a companion website offering extended resources for teaching and applied research. Researchers and academics will find a consolidated, interdisciplinary framework linking theory with practical healthcare applications, ideal for advancing scholarship and innovation.Biomedica... and clinical engineers gain actionable insights into system design, sensor integration, and clinical validation for building reliable, patient-centered solutions. Healthcare AI engineers and data scientists will benefit from specialized guidance on deploying predictive models, managing multi-sensor data fusion, and ensuring privacy-compliant, real-time analytics. This book empowers stakeholders across the healthcare ecosystem to drive the next generation of intelligent, adaptive, and trustworthy digital health systems.
Digital Twins
Core Principles and AI Integration
1st Edition
June 1, 2026
Bedir Tekinerdogan + 1 more
English
Digital Twins: Core Principles, System Engineering, and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. 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 discusses 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. 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.
AI Platforms as Global Governance for the Health Ecosystem
The Future's Global Hospital
1st Edition
May 1, 2026
Dominique J. Monlezun
English
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 to create global governance for the healthcare ecosystem. The book explores how AI platforms can transform hospitals and clinical practice by digitally unifying patients, providers, and payors, advancing healthcare for all. Users will find content that defines and explains the main hurdles and technical innovations in responsibly governing AI platforms for efficient, equitable, and sustainable global healthcare.Additiona... sections delve into the history, science, politics, economics, ethics, policy, and future of these AI platforms, and how governance efforts can work toward the common good. 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.
Artificial Intelligence and Machine Learning for Safety-Critical Systems
A Comprehensive Guide
1st Edition
May 1, 2026
Rajiv Pandey + 3 more
English
Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource on the challenges and mitigation strategies involved in their design. The book's authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models. In essence, the goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety.
Metaverse and AI in Healthcare
A Federated Learning Approach
1st Edition
May 1, 2026
Jyotir Moy Chatterjee + 1 more
English
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
Engineering Requirements and Political, Economic, and Philosophical Challenges
1st Edition
February 1, 2026
Mirko Farina + 3 more
English
The last decade has seen exponential growth in the development of digital technologies. This has led to significant shifts in the political arena as well as in the economy, precipitating a series of revolutionary changes in the fabric of our societies, which have had far-reaching consequences and effects on the way we relate and connect to each other. Digital Transformation in Artificial Systems: Engineering Requirements and Political, Economic, and Philosophical Challenges focuses on analyzing the engineering requirements as well as the political consequences, overarching the philosophical and ethical implications of this transformation, especially in relation to its application in artificial systems. In this context, the concept of digital transformation (understood as the practice of redefining models, functions, operations, processes, and activities by leveraging technological advancements to build efficient digital environments) has become increasingly important. This book brings together key concepts, ideas, and frameworks related to this idea. It promotes an inclusive and responsible digital transformation capable of addressing the constraints on the global digital divide, deepening cooperation in digitization, industrialization, and innovation, while furthering our understanding of the ethical and moral challenges associated with such a development. The distinctive and most original element of the book is its interdisciplinarity. It will allow readers to gather crucial insights that will be instrumental to better understand the reach of the forthcoming AI revolution, its multidimensionality, and its potential impact on people and society.
Edge Intelligence
Advanced Deep Transfer Learning for IoT Security
1st Edition
January 16, 2026
Jawad Ahmad + 5 more
English
Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.
Challenges and Applications of Generative Large Language Models
1st Edition
January 9, 2026
Anitha S. Pillai + 2 more
English
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.
Multilevel Quantum Metaheuristics
Applications in Data Exploration
1st Edition
January 9, 2026
Siddhartha Bhattacharyya + 4 more
English
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.