Books in Artificial intelligence general

Federated Learning for the Metaverse

1st Edition
January 11, 2026
Noor Zaman Jhanjhi + 3 more
English

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

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.

Multilevel Quantum Metaheuristics

1st Edition
January 1, 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.

Challenges and Applications of Generative Large Language Models

1st Edition
January 1, 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.

Decision Systems

1st Edition
January 1, 2026
Pallavi Vijay Chavan + 3 more
English

Decision Systems: Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks provides readers with a comprehensive understanding of the principal techniques used to build effective decision-making systems. This book covers the fundamental principles and concepts of machine learning, fuzzy logic, and artificial neural networks, and explains how these techniques can be used to build intelligent decision-making systems that can learn from data, reason, and make accurate predictions. The book also presents a wide range of applications of machine learning, fuzzy logic, and artificial neural networks in various domains, such as engineering, medicine, finance, and robotics. The book also provides practical guidance on how to design and implement effective decision-making systems using these techniques and discusses the potential challenges and limitations of machine learning, fuzzy logic, and artificial neural networks, and how to overcome them. The book provides a stepwise approach to provide readers with the knowledge and tools they need to build intelligent decision-making systems, including a robust introduction to the mathematical concepts and principles necessary to understand the concepts and applications of Decision Systems and Machine Learning algorithms. Next, the book provides readers with an in-depth explanation and demonstration of two of the major machine learning techniques – Fuzzy Logic/Fuzzy Set Theory and Artificial Neural Networks – followed by an in-depth look at more advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged Recurrent Networks and their mathematical foundations, Associative Memories, and Deep Learning networks such as Convolutional Neural Networks, Generative Adversarial Networks, Radial Basis Function Networks, Multilayer Perceptrons, and Self-Organizing Maps. The lynchpin of the book provides readers with an understanding of how the various types of techniques can be integrated to create dynamic Decision Systems. The book wraps up with coverage of challenges and opportunities in Decision Systems along with real-world applications of Decision Systems with case studies in healthcare, finance, education, social media, and agriculture.

Advanced Intelligence Methods for Data Science and Optimization

1st Edition
December 1, 2025
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.

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 explores the intricate connections between water, energy, and food—essentials for sustainable development—and the transformative potential of artificial intelligence (AI) within these domains. It begins by introducing the water-energy-food nexus, outlining the three dimensions: water-energy, water-food, and energy-food nexus. The book discusses assessment methods, challenges, opportunities, trends, and future perspectives. Subsequent chapters delve into AI techniques like machine learning, natural language processing, computer vision, and robotics. It studies AI applications in water treatment, wastewater management, renewable energy systems, smart grids, and food industry processes such as sorting, detection, safety, quality analysis, and packaging.The final chapter integrates AI applications across all these nexuses, making the book indispensable for researchers, students, engineers, 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.

Digital Transformation in Artificial Systems

1st Edition
November 1, 2025
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.

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