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Books in Machine learning

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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.

    • 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.

    • 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.

    • 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.

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

      • 1st Edition
      • March 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.

    • The Governance of Artificial Intelligence

      • 1st Edition
      • April 1, 2026
      • Tshilidzi Marwala
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 3 2 2 1
      • eBook
        9 7 8 0 4 4 3 3 6 3 2 3 8
      Artificial Intelligence governance is complicated by the fast pace of technological progress, the opaqueness of AI algorithms, worries about bias and impartiality, the requirement for accountability in AI-based decisions, and the global nature of AI development and deployment. The issue of AI governance is a developing one, and The Governance of AI is the first book that covers all the key topics in one book: AI values, Data, Algorithms, Computing, Applications, and Governance. The Governance of AI provides top-level guidance on all these topics from an engineering and governance perspective, while proposing a unifying framework for AI governance. An essential approach to AI governance is a proactive and comprehensive strategy that efficiently balances innovation and ethical concerns. The strategy presented in this book prioritizes social welfare and upholds human rights by maximizing the benefits of AI while reducing its negative aspects. In order to address these issues, it is essential to implement a versatile governance structure that incorporates several fields of study and encourages diversity. Additionally, utilizing existing regulatory frameworks, ethical standards, and industry benchmarks is essential. Moreover, it is crucial to integrate cooperation between governments, economic organizations, civil society, and the academic community under a multi-stakeholder framework to promote transparency, accountability, and public trust in AI systems. Furthermore, it is imperative to cultivate global cooperation in regulating AI because AI technology and its impacts extend beyond national boundaries. AI governance involves establishing worldwide norms and standards that encourage coordinating governance efforts while recognizing cultural and geographical differences. The Governance of AI is structured into six distinct sections and comprises 33 chapters. The first section comprises the chapters that address the principles that govern artificial AI. The second section has chapters that specifically address data-related topics. The AI algorithms are discussed in the third section. The fourth section has chapters that address the issue of computing. The fifth section has chapters that specifically address applications. The sixth section has chapters that address the topic of AI governance.

    • The AI Ideal

      • 1st Edition
      • April 1, 2026
      • Niklas Lidströmer
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 9 7 2 7
      • eBook
        9 7 8 0 4 4 3 4 4 9 7 3 4
      The AI Ideal: Aidealism and the Governance of AI, Dr. Niklas Lidströmer presents a bold alternative to the prevailing AI doom narratives. While nearly all other experts focus solely on warning of catastrophe, Dr Lidströmer also offers an actionable vision for ensuring AI strengthens democracy, ethics, and human dignity. Instead of allowing AI to concentrate power in the hands of a few, he argues for a new global framework—one where AI serves justice, enlightenment, and human betterment. Rooted in European Enlightenment ideals, Scandinavian social model and liberalism, and Swiss direct democracy, Aidealism rejects extreme ideologies and champions pragmatic, ethical, and forward-thinking solutions. From free education and healthcare to AI-driven economic justice and climate responsibility, this book explores how AI can help build a sustainable, free, and prosperous world—if we act now. Yet Aidealism does not promise utopia. The risks are real. The threats are mounting. AI could empower autocrats, disrupt economies, and undermine human agency. But it could also be our greatest tool for wisdom, fairness, and progress—if governed with foresight and courage. This book explicitly gives a manifesto for practical action. An action plan for how to harness and use AI for the common good, so that it benefits us all, rather than the few. The book elaborates on the daily conundrums of the human species; our nature, origins, goodness and cruelty, memes, hierarchies, political structures and how to build a fairer, more just, peaceful and benevolent society. It also tries to explain the core of AI for the general audience. It delves into a very broad range of areas, from philosophy to music, politics to ethics, and mathematics and physics to sociology and medicine. It tries to usher in an technological Enlightenment to save us from the threat of a malign and Machiavellian use of AI. This is not another AI dystopia, nor is it blind optimism. It is a manifesto for action—a call to use AI not just to enhance intelligence, but to make humanity nobler. The AI revolution is not something happening to us—it is something happening through us. The only question is: Will we build wisely? For those seeking a visionary, constructive, and ethically grounded roadmap for AI—one written in the spirit of true idealism—this book is essential reading.

    • 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.

    • Deep Learning Assessment of Neurological Imaging

      • 1st Edition
      • July 1, 2025
      • Tripti Goel + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 2 9 1 6
      • eBook
        9 7 8 0 4 4 3 3 0 2 9 2 3
      "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.