Skip to main content
Elsevier
View Cart

Books in Machine learning

BooksJournals

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

      • 1st Edition
      • Manuel González Canché
      • English
      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.

    • Federated Learning for the Metaverse

      Applications in Virtual Environments
      • 1st Edition
      • 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.

    • Advanced Intelligence Methods for Data Science and Optimization

      • 1st Edition
      • Amir Hossein Gandomi + 2 more
      • English
      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.

    • The Governance of Artificial Intelligence

      • 1st Edition
      • Tshilidzi Marwala
      • English
      The Governance of Artificial Intelligence stands out as a comprehensive guide that unifies the essential dimensions of artificial intelligence—values, data, algorithms, computing, applications, and governance—within a single volume. The book offers expert guidance on each of these topics, blending engineering insight with governance strategies. It proposes a holistic approach to AI governance, emphasizing the importance of proactive and balanced policies that foster innovation while safeguarding ethical standards. Prioritizing social welfare and human rights, this work advocates for maximizing AI’s benefits and minimizing its risks through effective, integrative governance structures.Moreover, the book highlights the need for a versatile governance model that draws from various disciplines and champions diversity. It stresses the importance of leveraging existing regulatory frameworks, ethical guidelines, and industry standards, while encouraging active collaboration among governments, businesses, civil society, and academia. Structured into six sections and 33 chapters, the book systematically explores core principles, data concerns, algorithms, computing, practical applications, and governance challenges, making it a crucial resource for understanding the evolving landscape of AI oversight.

    • The AI Ideal

      AIdealism and the Governance of AI
      • 1st Edition
      • Niklas Lidströmer
      • English
      The AI Ideal: AIdealism and the Governance of AI reimagines the role artificial intelligence can play in shaping our future. Rather than warning of catastrophe, Lidströmer presents a proactive vision—one where AI strengthens democracy, ethics, and human dignity through a global framework rooted in European Enlightenment ideals, Scandinavian social values, and Swiss direct democracy. His approach, dubbed AIdealism, rejects extreme ideologies and advocates for pragmatic, ethical solutions, aiming to use AI for justice, enlightenment, and the common good. This book calls for action in harnessing AI to create a world that is sustainable, free, and prosperous for all.Far from promising utopia, the book acknowledges the risks AI poses—empowering autocrats, disrupting economies, and threatening human agency. Yet, it argues that with foresight and courage, AI can become a powerful tool for wisdom, fairness, and progress. It offers a practical action plan for using AI to address daily dilemmas, from education and healthcare to climate responsibility, while exploring topics as varied as philosophy, politics, ethics, music, mathematics, and medicine. Ultimately, it serves as a visionary, ethically grounded manifesto for those seeking a constructive roadmap for the AI revolution.

    • Digital Supply Chain Transformation

      Implementing Technology, Analytics, and Data-Driven Solutions
      • 1st Edition
      • Vinaytosh Mishra
      • English
      Digital Supply Chain Transformation: Implementing Technology, Analytics, and Data-Driven Solutions offers an in-depth exploration of how digital innovation is reshaping modern supply chain management. The book seamlessly blends theoretical concepts with practical application, ensuring readers not only grasp fundamental supply chain principles but also see how technologies like machine learning, AI, and system dynamics modeling are transforming the industry. Readers are guided through the evolving landscape of supply chains, learning to harness digital tools and analytics for improved efficiency, accuracy, and end-to-end visibility in operations.This comprehensive resource equips readers to tackle real-world challenges by applying advanced technologies and analytics to supply chain problems. It highlights the growing importance of data-driven decision-making, encourages critical evaluation of strategies and solutions, and anticipates emerging trends and future opportunities. By merging theory with hands-on practice, the book enables professionals to drive innovation and recommend informed improvements in the rapidly advancing field of supply chain management.

    • Learning-Driven Game Theory for AI

      Concepts, Models, and Applications
      • 1st Edition
      • 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.

    • Edge Intelligence

      Advanced Deep Transfer Learning for IoT Security
      • 1st Edition
      • 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.

    • IoT Security

      Fundamentals and Key Enabling Technologies
      • 1st Edition
      • SK Hafizul Islam + 3 more
      • English
      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

      Algorithms, Systems, and Applications
      • 1st Edition
      • Long Cheng + 2 more
      • English
      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.