Books in Machine learning

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

1st Edition
February 1, 2026
Manuel González Canché
English
Paperback
9 7 8 0 4 4 3 2 1 9 6 1 0
eBook
9 7 8 0 4 4 3 2 1 9 6 0 3

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools—just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

Medical Imaging and Behavioral Science

1st Edition
February 1, 2026
Dilber Uzun Ozsahin + 4 more
English

Medical Imaging and Behavioral Science provides comprehensive coverage of the fundamental principles of medical imaging and behavioral science. This book presents the latest research, techniques, and technological advancements in medical imaging and behavioral science for researchers and professionals working in the fields of image processing, computer science, and healthcare enabling them to solve problems at different stages of the research workflow. The book examines machine learning and deep-learning based approaches to medical image analysis. Chapters investigate the design and application of software and diagnostic tools across all aspects of medical imaging and behavioral sciences, including autism spectrum disorders, memory and learning, stress, eating disorders, among others. Researchers and professionals working in the fields of medical image processing, computer science, radiology, psychology, and neuroscience will benefit from the unique multidisciplinary perspective.

Learning-Driven Game Theory for AI

1st Edition
February 1, 2026
Mehdi Salimi + 1 more
English

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.

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.

Edge Intelligence

1st Edition
January 1, 2026
Jawad Ahmad + 5 more
English
Paperback
9 7 8 0 4 4 3 3 8 2 9 7 0
eBook
9 7 8 0 4 4 3 3 8 2 9 8 7

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.

IoT Security

1st Edition
October 1, 2025
SK Hafizul Islam + 3 more
English
Paperback
9 7 8 0 4 4 3 3 4 1 2 5 0
eBook
9 7 8 0 4 4 3 3 4 1 2 6 7

IoT Security: Fundamentals and Key Enabling Technologies explores the complex interactions between Internet of Things (IoT) and the pressing need for effective cyber security solutions. Diving into real-world case studies to provide insights into implementing efficient security measures that safeguard against online dangers, this book comprehensively analyzes the challenges and possibilities presented by intelligent technologies fueling transformational change, emphasizing the crucial role cybersecurity plays in defending networks, data, and user privacy in an increasingly interconnected digital ecosystem.Coverage includes cryptographic methods, communication networks, device identity and access, data governance and privacy, as well as regulatory frameworks and standards for IoT security. Computer science researchers and engineers will benefit from this compilation of cutting-edge research and practical case studies, learning how to minimize risks for IoT and intelligent technologies.

Quantum Computational AI

1st Edition
August 5, 2025
Long Cheng + 2 more
English
Paperback
9 7 8 0 4 4 3 3 0 2 5 9 6
eBook
9 7 8 0 4 4 3 3 0 2 6 0 2

Quantum Computational AI: Algorithms, Systems, and Applications is an emerging field that bridges quantum computing and artificial intelligence. With rapid advancements in both areas, this book serves as a vital resource, capturing the latest theories, algorithms, and practical applications at their intersection. It aims to be both informative and accessible, making it perfect for academics, researchers, industry professionals, and students eager to lead in these technologies. The book explores quantum algorithms, system design, and demonstrates real-world applications across various sectors. It provides a comprehensive understanding of how quantum principles can advance AI, revealing unprecedented possibilities and benefits.

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

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.

Deep Learning Assessment of Neurological Imaging

1st Edition
July 1, 2025
Tripti Goel + 3 more
English

"Deep Learning Assessment of Neurological Imaging" provides an introduction to deep learning structures and pre-processing methods for detecting MRI anomalies. It also provides a comprehensive account of deep learning research on MRI images for Alzheimer's disease, Parkinson's disease, and schizophrenia, and a discussion on current research issues and future objectives. The book is a valuable resource to guide new entrants in the field to make a meaningful impact in their development efforts. The book concludes with a brief overview of the problems discussed and potential future advancements in the field.

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.

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