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Elsevier

Books in Artificial intelligence

Our AI collection covers machine learning, natural language processing, robotics, and intelligent systems. Showcasing the latest algorithms, theoretical foundations, and real-world applications, these titles support researchers, practitioners, and students in advancing AI technologies. Emphasizing ethical considerations, explainability, and innovation, the content addresses challenges in automation, data analysis, and decision-making. This comprehensive portfolio fosters breakthroughs that shape the future of intelligent systems and their societal impact.

  • AI Challenges for a Sustainable Future

    • 1st Edition
    • Kuldeep Kaswan + 2 more
    • English
    Artificial Intelligence (AI) has emerged as a fundamental transformative force which can create revolutionary solutions to protect the environment while encouraging economic development and improving social progress. AI also brings potential threats for the planet and for future generations. AI Challenges for a Sustainable Future analyzes the role of AI in promoting sustainability while also addressing the significant challenges it introduces which threaten long-term ecological and social equilibrium. It examines the impact of AI biases, along with energy use optimization challenges and social economic problems that result from AI applications. The book presents analysis of natural resource governance and sustainable computing development, and offers solutions to aid in the achievement of worldwide Sustainable Development Goals (SDGs), and questions how we can control AI technology before it undermines future developments. Each chapter explores a key issue, highlights obstacles, and suggests technological, ethical, and policy-oriented strategies to lessen AI's adverse effects while enhancing its advantages for a sustainable future. The book advocates for energy-efficient AI algorithms, green computing, and the development of sustainable hardware. It also recommends decentralized AI models to decrease dependence on energy-intensive cloud computing. Additionally, it discusses policy frameworks aimed at fostering responsible AI development and achieving carbon-neutral AI systems.

  • 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.

  • Advances in Multimodal Large Language Models for Healthcare

    Methods and Applications
    • 1st Edition
    • Hari Mohan Pandey + 4 more
    • English
    Advances in Multimodal Large Language Models for Healthcare: Methods and Applications provides valuable insights on Large Language Models in healthcare applications for researchers, academics, and practitioners. The book explains key concepts, including artificial intelligence, machine learning, deep learning, and the evolution of neural networks and transformer models. It then covers generative AI and LLMs for a wide spectrum of healthcare applications, including mental health, clinical decision support, interactive system design, and sensitive analysis. Readers will find this to be a valuable deep dive into the emergent intersection of LLMs and health care, with guidance into applications, technical and programming methods, and more.Although LLMs have shown some promising results in the healthcare sector, numerous challenges need to be addressed before they can be used in patient care. The two key issues with the adoption of LLMs regarding healthcare settings are reliability, transparency, interpretation of results and bias (data and algorithm) management. Unless properly and adequately validated, there may be incorrect medical information provided by the LLM-based systems, which can lead to misdiagnosis or hazardous treatment errors. At this point, LLMs have not only been used for decision making or documentation, they have also proven to be useful in patient engagement through QA systems, medical chatbots, and virtual healthcare.

  • Principles of Medical Biohybrid Microrobots

    • 1st Edition
    • Veronika Magdanz + 1 more
    • English

  • Trustworthy LLMs

    Principles and Challenges
    • 1st Edition
    • Mohit Kumar
    • English
    Despite numerous advantages, LLMs have trust, transparency, accountability, and reliability issues due to development with "black-box" approaches, which make it difficult to understand how LLMs create specific outputs. Trustworthy LLMs: Principles and Challenges presents the fundamental concepts of trustworthy LLMs, then proceeds to address the foremost challenges researchers and developers face in developing reliable and trustworthy LLMs. The book begins by presenting the main research branches of artificial intelligence along with the principles of LLMs, from pre-training to fine tuning, and, ultimately, trustworthy LLMs. Readers will learn about the chief technical principles of LLMs, including attention mechanism, transformers, and transfer learning. The methodologies used for development of ChatGPT have been explained as a case study for comprehensive understanding of the concepts involved in LLMs. Readers will also learn about the integration of XAI with LLM, and other key frontiers in trustworthy LLM development, including the synergy between deep learning and LLMs, as well as case studies on GPT-4 and OPT-1.3B. The book concludes with chapters on key challenges and future research approaches for developing trustworthy LLMs.

  • Artificial Intelligence and Machine Learning for Safety-Critical Systems

    A Comprehensive Guide
    • 1st Edition
    • 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.

  • Integrating AI in Psychological and Mental Health Care

    Techniques, Applications, and Ethical Considerations
    • 1st Edition
    • Sandeep Kautish + 4 more
    • English
    Integrating AI in Psychological and Mental Health Care: Techniques, Applications, and Ethical Considerations introduces key concepts and the historical evolution of AI, providing a foundation for understanding its applications in mental health. The content delves into various aspects of AI, including diagnostic tools, machine learning algorithms, and natural language processing, highlighting their roles in enhancing therapeutic outcomes and improving patient care. The discussion encompasses significant mental health conditions such as anxiety, depression, and severe psychological disorders, showcasing how AI technologies can assist in diagnosis, treatment planning, and monitoring. Ethical considerations and privacy issues are critically examined, ensuring a balanced perspective on the benefits and challenges associated with AI-driven interventions. Practical applications, such as virtual psychotherapists and AI-enhanced cognitive behavioral therapy illustrate real-world implementations and their impact on patient care. Additionally, case studies provide insights into successful AI applications in mental health settings, thus enhancing our understanding of potential advantages and obstacles.

  • System of Systems Engineering

    Innovations, Challenges, and Future Directions
    • 1st Edition
    • Bedir Tekinerdogan + 1 more
    • English
    System of Systems Engineering: Innovations, Challenges, and Future Directions focuses on the many aspects of System of Systems Engineering. Part I, Foundations of System of Systems Engineering, introduces the field, characterizes and classifies SoS, and discusses key concepts. Part II, Governance and Management of SoSE, covers strategic governance, policy and regulatory frameworks, and leadership and decision-making in SoSE projects. Part III, Methodologies and Tools, explores systems thinking and modeling approaches, lifecycle management, and interoperability and integration strategies. Part IV, AI and System of Systems Engineering, delves into leveraging AI for enhanced decision-making, machine learning applications, AI-driven automation and control, and ethical considerations.Final... Part V, Case Studies and Emerging Challenges, presents real-world applications in defense and aerospace, smart cities, healthcare, environmental and energy systems, and discusses future directions and research opportunities. This book offers significant benefits to graduate students, researchers, and professionals in software engineering, systems engineering, aerospace engineering, defense, telecommunications, and other fields where SoSE is relevant.

  • Measuring the User Experience

    Collecting, Analyzing, and Presenting UX Metrics
    • 4th Edition
    • Bill Albert
    • English
    Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics provides the quantitative analysis training that students and professionals need. This book presents an update on the first resource that focused on how to quantify user experience. Now in its fourth edition, the authors have continued to expand on the area of behavioral and physiological metrics. The book also contains new research and updated examples, several new case studies, and new examples using the most recent version of Excel.

  • UX of AI

    Understanding and Designing Intelligent User Experiences
    • 1st Edition
    • Rahul Divekar
    • English
    Artificial Intelligence (AI) has become so ubiquitous within technology that all technological design involves some aspect of AI behind the scenes. Knowing how AI technologies relate to the user experience (UX)/user interface (UI)/product design field and how they can be applied will be a key skill for both designers and technologists. The UX of AI: Understanding and Designing Intelligent User Experiencesoffers a critically understanding of the benefits and limitations of the integration of AI and UX. Bridging the gap between artificial intelligence technologies and experience design, and written for UX/UI/product/servic... designers, researchers, and students, it demystifies complex AI concepts and demonstrates their practical applications in creating intuitive, responsive, and personalized user experiences. The book begins by establishing fundamental AI concepts in accessible language, avoiding unnecessary technical jargon while providing readers with a solid understanding of Large Language Models (LLMs), machine learning, natural language processing, computer vision, and complex architectures that combine various AI technologies to form innovative interactions and products. It then explores the unique design challenges and opportunities presented by AI-powered interfaces. The book also offers exercises that instructors can use as interactive classroom activities or homework assignments.

  • Deep Learning Approaches for Healthcare Data Analysis and Decision Making

    • 1st Edition
    • Ashish Bagwari + 5 more
    • English
    Deep Learning Approaches for Healthcare Data Analysis and Decision Making demystifies complex data-driven technologies, providing a clear framework for integrating advanced analytics into healthcare practices. With a focus on practical applications, the authors present a comprehensive digital transformation methodology that empowers readers to tackle the multifaceted challenges of healthcare data management. By leveraging deep learning techniques, readers will learn to analyze vast datasets, identify critical patterns, and develop predictive models that enhance diagnosis and treatment strategies while ensuring compliance with stringent data regulations. The book also addresses the pressing need for ethical AI practices, emphasizing patient privacy and data security.Real-world case studies illustrate how to implement personalized healthcare solutions and foster interdisciplinary collaboration, breaking down silos in knowledge and practice. Moreover, it explores innovative business models for sustainable AI integration, offering actionable insights for healthcare providers. This resource equips professionals with the tools to drive innovation, improve patient outcomes, and navigate the complexities of digital transformation in healthcare, making it a must-read for anyone at the intersection of technology and healthcare.

  • Soft and Continuum Robots for Medical Applications

    A Practical Approach
    • 1st Edition
    • Manish Chauhan + 1 more
    • English
    Soft and Continuum Robots for Medical Applications: A Practical Approach introduces present and future technologies of soft and continuum robotics for medical applications. This book dives into the importance of medical device development for electronics, control theory, microcontrollers, MATLAB programming, machine design, biophysics, anatomy, and physiological limitations through a structured approach that clearly presents underlying key concepts to educate readers on the methodology of approaching medical device development. This book also explores soft and continuum robotic technology in various sub-domains such as robotic navigation, robotic navigation, sensing and bio photonics, autonomy, and shared autonomy. Biomedical engineers, doctors, and researchers in biomedical robotics, biomechanics, robotics, biotechnology, engineering in medicine, mechanical and electrical engineering, health science, and technology will find this book helpful in providing theoretical and practical knowledge in using robotics in the medical field.

  • Moving Towards Fully Autonomous and Cost-Effective Surgical Robotic Systems with AI

    • 1st Edition
    • Tawanda Mushiri + 3 more
    • English
    Moving Towards Fully Autonomous and Cost-Effective Surgical Robotic Systems with AI offers advanced insights into how AI can be used to optimize the design, manufacturing, and clinical application of these systems. It covers a wide range of topics, including the introduction of surgical robotic systems design and the presentation of a fully autonomous surgical robotic system for minimally invasive surgery. Advanced medical imaging techniques integrated with surgical robots are discussed to enhance surgical precision and safety. The book also highlights the application of machine learning and deep learning models in robotic-assisted surgery, with the development of novel algorithms for autonomous surgical robots. Various AI approaches for optimizing surgical robotic systems and the design considerations for manufacturing and assembly are examined. Additionally, the book includes a thorough analysis of the life cycle and economic aspects of surgical robotic systems, as well as risk assessment and evaluation methods. The final chapters address ethics considerations and explore future enhancements in the realm of fully autonomous surgical robotic systems. Overall, this book aims to contribute to the technical understanding, design aspects, and ethical dimensions of surgical robotics, promoting safer and more efficient surgical procedures.

  • Advanced Methods for Optimizing and Accelerating Deep Learning Models

    • 1st Edition
    • Patrick Siarry + 3 more
    • English
    Advanced Optimization and Acceleration Techniques for Deep Learning Models provides a comprehensive guide to enhancing deep learning models' efficiency, scalability, and performance, including large language models (LLMs). As AI systems grow in complexity, optimizing their training and deployment has become critical for achieving higher accuracy, faster inference, and reduced computational costs. This book explores cutting-edge optimization strategies, from gradient descent refinements and hyperparameter tuning to model compression, pruning, and hardware acceleration. AI is evolving rapidly, but existing deep learning resources often focus on building models rather than optimizing them for efficiency and scalability. As deep learning applications expand into cloud computing, edge AI, and real-time decision-making, a dedicated resource on optimization is essential. This book addresses this gap by providing a structured approach to making deep learning networks faster, more cost-effective, and more sustainable.

  • Functional Equations and Stability Theory

    Methods and Applications in Science and Engineering
    • 1st Edition
    • Ali Turab + 2 more
    • English
    Functional Equations and Stability Theory: Methods and Applications in Science and Engineering presents current advancements in the theory of functional equations and their stability, with a particular emphasis on their applications across science, engineering, and computational domains. Functional equations—long-stand... pillars of mathematical analysis—have experienced a transformative evolution through the lens of Hyers–Ulam stability theory and its generalizations, which now intersect with key disciplines including artificial intelligence, cognitive science, and cybersecurity. This book provides readers with both principles of classical theory along with contemporary applications, thereby advancing the discourse on functional equations beyond its traditional confines. The book provides readers with both foundational studies and emerging methodologies, exploring how stability properties—such as Ulam–Hyers and generalized stability—are not only central to pure mathematics but also critically relevant to the modeling of real-world systems. By presenting methods that span theoretical results, fixed point methods, approximation techniques, and interdisciplinary case studies, this book offers a comprehensive perspective for readers interested in both mathematical rigor and real-world impact.The book features chapters on advanced topics including classical and generalized stability of Cauchy, Jensen, Drygas, and Pexider-type equations; functional equations in restricted domains and abstract metric spaces; fixed point techniques for analyzing nonlinear and operatorial equations; stability in functional inequalities and mixed-type systems; applications to cognitive modeling, behavioral dynamics, and learning systems; functional frameworks in information theory, entropy, and communication systems; operator-valued and matrix functional equations; stability-informed modeling in cybersecurity, robotics, and intelligent environments; and, approximation and numerical approaches to stability problems. As such, the authors address both computational and mathematical dimensions, empowering readers to apply functional equations and stability theory to contemporary scientific problems.

  • Digital Twins and Predictive Modeling in Robotic Surgery

    • 1st Edition
    • Mudassir Khan + 3 more
    • English
    Digital Twins and Predictive Modeling in Robotic Surgery addresses the transformative convergence of digital twin technology, predictive analytics, and robotic surgery. This reference responds to the critical need for a comprehensive resource that bridges simulation science, artificial intelligence, and real-time robotic execution, facilitating enhanced surgical precision, patient-specific planning, and intraoperative decision-making. The content is organized into four parts covering foundational intelligent surgical systems, predictive modeling and simulation, clinical applications with case studies, and challenges alongside future directions. Topics include AI integration, IoT-enabled sensor networks, digital twin architectures encompassing the entire surgical ecosystem, surgical workflow modeling, risk assessment, human-robot interaction, technology acceptance, ethical and regulatory considerations. Real-world examples from smart hospitals worldwide illustrate practical implementation. This books benefits biomedical engineers, researchers, and healthcare system architects by providing interdisciplinary insights, practical implementation frameworks, and cutting-edge case studies. It supports improved surgical planning, precision, and decision-making, empowering end users to advance the adoption of intelligent, AI-powered robotic surgery systems in clinical practice and research environments.

  • Observing the User Experience

    A Practitioner's Guide to User Research
    • 3rd Edition
    • Elizabeth Goodman + 1 more
    • English
    Observing the User Experience: A Practitioner's Guide to User Research, Third Edition helps readers bridge the gap to understand what users want and need from their product. Filled with real-world experience and a wealth of practical information, the book presents a complete toolbox of techniques to help designers, developers, and other stakeholders see through the eyes of their users. Sections discuss the benefits of end-user research and the ways it fits into the development of useful, desirable, and successful products and present techniques for understanding people’s needs, desires, and abilities, providing a basis for developing better products, whether Web, software, or mobile-based.Final chapters explain the communication and application of research results.

  • Probabilistic Modelling for Advanced Data Analysis

    • 1st Edition
    • Amit Kumar Tyagi + 1 more
    • English
    Probabilistic Modelling for Advanced Data Analysis provides a practical and rigorous guide for data practitioners to effectively implement probabilistic models in real-world scenarios. The book strikes a balance between high-level intuition and technical derivations, offering step-by-step explanations, real-world case studies, and Python implementation examples. The authors offer specific solutions that include modeling and quantifying uncertainty in data-driven decision-making, applying Bayesian inference to real-world problems and implementing scalable probabilistic models for large-scale datasets, all of which contribute to explainable and trustworthy AI.This book presents readers with theoretical foundations and practical applications of probabilistic modeling, providing a structured approach for researchers, data scientists, and industry professionals. It meets the increasing demand for uncertainty-aware AI models, Bayesian inference, and probabilistic graphical models across various fields of research.

  • Healthcare 5.0

    Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data
    • 1st Edition
    • Yugal Kumar + 2 more
    • English
    Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data addresses the urgent need for innovation in today’s complex healthcare data landscape that is characterized by pandemics, aging populations, and chronic conditions. The book introduces the concept of ‘Healthcare 5.0’ as an interconnected, data-driven, and patient-centric framework where advanced technologies such as AI, ML, IoMT, Big Data, and Large Language Models (LLMs) converge to optimize care, streamline operations, and deliver personalized, predictive solutions. The book moves from core AI applications in electronic health records, drug discovery, data management, and privacy, through cutting-edge big data analytics for disease forecasting and diagnosis.It explores new research advances in the Internet of Medical Things, including connected device architectures and their fusion with AI for dynamic decision-making. The third section focuses on data analytics in telemedicine, remote care, system usability, and integration in Healthcare 5.0. The personalized healthcare section details analysis and applications in AI- and IoT-powered assistance, and real-time monitoring. The last section explores the development of LLMs and their applications in medical imaging, clinical decision support, predictive analytics, system architectures, as well as the ethical challenges of their deployment in healthcare.

  • The Deterministic Universe

    Exploring Chaos, Free Will, Prediction, and Modeling
    • 1st Edition
    • Paul A. Gagniuc
    • English
    The Deterministic Universe: Exploring Chaos, Free Will, Prediction, and Modeling equips readers with the tools to learn the foundational concepts of chaos, randomness, and determinism through examples and applied case studies. The book helps readers gain insights into how deterministic algorithms handle complex, chaotic data, providing an interdisciplinary exploration of chaos theory, determinism, and free will grounded in scientific principles, computational models, and philosophical insights. The content builds on established theories in physics, bioinformatics, and systems biology, weaving them into broader existential questions. The material emphasizes the interplay between randomness, noise, and order, providing a fresh lens to view the universe.The book connects these ideas to practical tools like random number generators and nonlinear equations, machine learning algorithms, computational and predictive models, extending their implications to biological systems, human thought, and decision-making. By addressing both scientific fundamentals and philosophical debates, it bridges abstract ideas with real-world phenomena and demonstrates the role of randomness and noise in predictive models and simulations, thus helping readers understand the limits of computational systems in mimicking real-world processes.

  • Generative AI Risks and Benefits within Human-Machine Teams

    • 1st Edition
    • William Lawless + 1 more
    • English
    Generative AI Risks and Benefits within Human-Machine Teams delves into the foundational principles, metrics, and applications of human-machine systems, addressing the legal ramifications of autonomy, public trust, and bidirectional AI systems. This book brings together world-class researchers, engineers, philosophers, social scientists, and other experts to discuss the critical aspects of generative AI, its risks, and benefits. The authors aim to establish a shared context between humans and machines, regulators, the public, and other stakeholders, exploring how these systems impact targeted audiences and society at large. The book combines human-centered computing and autonomous human-machine teams to provide a comprehensive understanding of generative AI's potential and challenges. The book is structured to guide readers through a detailed exploration of these topics. It begins with an introduction to the core concepts of human-machine collaboration and the next generation of large language models. The discussion then moves to practical applications, such as Human-AI collaboration for energy communities, autonomous human-machine team advances, and human-AI collaboration in the design process. The book also delves into adaptive collaboration patterns for logic modeling, assessing multimodal large language models in resolving visual ambiguities, and developing team context-aware collaborative AI assistants. Additionally, it explores the taxonomy of teamwork support for collaborative AI efforts, trust management in human-AI collaboration, and provides a distributed teaming testbed for human-machine collaboration in space missions. Finally, it addresses the limits of classical team science in interdependent human-machine teams.Generative AI Risks and Benefits within Human-Machine Teams is an essential resource for computer scientists and systems engineers focused on designing and theorizing about the development of autonomous systems. By providing in-depth insights into the integration of generative AI within human-machine teams, this book equips professionals with the knowledge to navigate the complexities of AI autonomy, enhance collaboration, and address the ethical and technical challenges associated with these advanced technologies.

  • Deep Learning Models for Continuous Authentication on Mobile Devices

    • 1st Edition
    • Yantao Li + 3 more
    • English
    Sensor-based continuous authentication has emerged as a critical approach for strengthening mobile security, enabling persistent user verification without disrupting device usage. However, the field faces significant hurdles, including limited training data, complex feature representation, environmental noise, and the strict resource constraints of mobile hardware.Deep Learning Models for Continuous Authentication on Mobile Devices provides a unified and structured treatment of data-driven continuous authentication, presenting a systematic study of sensor-based continuous authentication on mobile devices, focusing on modern machine learning and deep learning techniques. It guides readers in designing, analyzing, and deploying reliable systems that effectively balance security, robustness, and computational efficiency. Featuring data augmentation strategies for data scarcity, multi-sensor feature fusion, discriminative feature learning via two-stream CNNs, data synthesis using conditional Wasserstein GANs, lightweight networks for efficient deployment, neural architecture search for automated optimization, and neuromorphic computing with spiking neural networks,Deep Learning Models for Continuous Authentication on Mobile Devices balances methodological rigor with practical system design, offering robust solutions for real-world mobile security.

  • Autonomous Intelligence

    Designing the Future of Agentic Systems
    • 1st Edition
    • Shubham Mahajan + 1 more
    • English
    Autonomous Intelligence: Designing the Future of Agentic Systems provides insights on how autonomous technologies—from robots to AI-driven systems— are transforming industries and daily life. The book explores how these intelligent agents operate independently, making decisions and taking actions without constant human intervention. Through clear explanations and real-world examples, the book serves as a gateway for both novices and experts as it simplifies the complexities of autonomous systems. Sections cover Foundations of AI and Autonomy; Understanding Agentic Intelligence; Technology Enablers and Architectures; Design, Safety, and Deployment; Sectoral Applications of Agentic AI; Societal, Ethical, and Future Considerations.The book also considers how these technologies function, while also addressing concerns about their integration, limitations, potential benefits, and possible risks. In a rapidly evolving field, the book looks at latest trends, applications, and research, and at the ethical and legal considerations, especially in areas like employment, privacy, and accountability.

  • Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support

    • 1st Edition
    • Manoj Diwakar + 3 more
    • English
    Multimodal Deep Learning and Data-Centric Systems for Smart Healthcare and Clinical Decision Support examines how to fuse imaging, genomics, electronic health records, and wearable sensor data into clinically actionable insights. As healthcare data becomes increasingly diverse and voluminous, there is a pressing need for integrative methodologies that preserve information across modalities while maintaining interpretability and safety. Current resources either focus on single-modality AI or domain-specific applications, leaving practitioners with fragmented guidance. This volume defines a cohesive, data-centric framework for multimodal predictive diagnostics and clinical decision support, addressing methodological foundations, reproducible pipelines, and real-world translation challenges.

  • Advanced Concepts in Grey Wolf Optimizer

    Leading the Pack in Advanced Optimization
    • 1st Edition
    • Seyedali Mirjalili
    • English
    Advanced Concepts in Grey Wolf Optimizer: Leading the Pack in Advanced Optimization provides in-depth coverage of recent theoretical advancements in GWO, as well as advanced methods to handle issues such as multiple objectives, constraints, binary variables, large search spaces, dynamic goals, and uncertain data. This book assumes familiarity with optimization fundamentals and therefore dives directly into multi-objective, constrained, binary, and dynamic-environment variants, as well as GWO-ML/LLM hybrids. Extensive real-world case studies in areas such as energy systems, supply-chain design, LLM fine-tuning, robotics, and finance ensure that both scholars and engineers can translate the material into deployable solutions. The authors present important new theories, hybrids with Machine Learning/Deep Learning, and hybrid methods that increase GWO’s performance. The use of generative AI to improve this algorithm and make it more generic is also explored, along with diverse applications across multiple fields to illustrate the practical utility and versatility of the methods presented. Written by some of the world’s most highly cited researchers in the field of artificial intelligence, algorithms, and machine learning, the book serves as an advanced resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

  • Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure

    • 1st Edition
    • V. Subramaniyaswamy + 3 more
    • English
    Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure offers a comprehensive exploration of how AI and data science are revolutionizing the electric vehicle (EV) industry. It guides readers through the basic concepts of EV technology and explains how machine learning and blockchain optimize battery management, predictive maintenance, and secure fault detection. The book highlights cutting-edge techniques like sensor fusion and computer vision for autonomous driving, alongside real-time analytics and edge computing for low-latency AI applications. It also covers intelligent charging infrastructure, route optimization, and renewable energy integration and shares insights into cybersecurity, business models, and demand forecasting, complemented by practical case studies.This book is a useful resource for researchers, scientists, advanced students, software engineers, data scientists, R&D professionals, and other industrial personnel working at the intersection of computer science, electrical engineering, artificial intelligence, data science, and machine learning with an interest in advancing AI and ML applications in electric vehicle technologies.

  • Robotics for Intervention in Healthcare

    From Technology to Clinical Practice
    • 1st Edition
    • Françoise J Siepel
    • English
    Robotics for Intervention in Healthcare: From Technology to Clinical Practice bridges the gap between deep-core robotic intervention technology and clinical aspects, including content that is appropriate for physicians and clinicians. The book gives insights on the importance of connectivity in early stages, thoroughly addressing which aspects are important to improve the innovation chain.

  • Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

    • 1st Edition
    • Hanif Heidari + 1 more
    • English
    Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders discusses the latest developed methods in IoT and its applications in neurological disorders that emphasize end-user requirements. Intelligent IoT is used to explore the intersection between medicine, data science, biomedical engineering, and healthcare systems. A comprehensive overview of modelling and analyzing the requirements of people with neurological disorders is presented in this book. Signals and images of biological activity are collected and analyzed based on patient specifications to facilitate more accurate diagnosis and treatment. The book also discusses cutting-edge AI methods for IoT devices designed to treat neurological conditions.

  • AI-Driven Diagnostics for 6G-Enabled Smart Healthcare

    • 1st Edition
    • Sangeeta Kumari + 3 more
    • English
    AI-Driven Diagnostics for 6G-Enabled Smart Healthcare explores the transformative integration of artificial intelligence (AI) and next-generation 6G networks in the healthcare sector. The book begins by highlighting the evolution of healthcare technology and the critical role of AI-driven diagnostics, emphasizing how 6G facilitates real-time, ultra-reliable communication. Key features of 6G, such as ultra-low latency and massive connectivity are discussed, showcasing their impact on advanced healthcare applications like remote diagnostics and patient monitoring. In addition, the integration of AI in medical diagnostics is examined, focusing on machine learning and deep learning techniques that enhance disease detection through medical imaging and clinical data analysis.Users will also find content that explores the benefits of remote patient monitoring, particularly for underserved populations, and delves into edge AI for localized, low-latency diagnostic processing. Other sections cover real-time imaging diagnostics are highlighted and address predictive analytics, detailing AI models that forecast diseases and the role of IoT devices and wearables in healthcare diagnostics. Final sections cover ethical considerations and regulatory challenges.

  • Blockchain-Enabled Innovations in Location-Based Services for Healthcare

    • 1st Edition
    • Ajay Kumar Gupta + 3 more
    • English
    Blockchain-Enabled Innovations in Location-Based Services for Healthcare addresses the growing need for secure, intelligent healthcare solutions integrating location-based services (LBS), Internet of Things (IoT), and blockchain technologies. This reference explores how these technologies transform healthcare delivery improving autonomy and real-time care while safeguarding sensitive data. It responds to the challenges posed by the proliferation of wearable sensors and smart medical devices by proposing decentralized, privacy-preserving frameworks.The book’s comprehensive chapters cover blockchain trust models, adaptive privacy for patient trajectories, wearable computing, deep learning for medical imaging, secure communication in healthcare IoT, and AI-powered diagnostics. Contributions from global experts provide case-driven analyses and technical depth, addressing current challenges in healthcare data security, interoperability, and predictive modeling. The interdisciplinary approach integrates computer science, data privacy, and healthcare informatics, making it a valuable resource for researchers and professionals.Blockc... Innovations in Location-Based Services for Healthcare benefits academic researchers, industry practitioners, graduate students, and policymakers by offering cutting-edge knowledge in secure, decentralized healthcare systems. It equips readers with advanced methodologies and practical solutions to design inclusive healthcare technologies, fostering innovation in smart health monitoring, medical image analysis, and data-driven diagnostics within privacy-compliant frameworks.

  • Computational Intelligence in Mechatronics

    Solving Real-World Problems in Electronic Systems Design
    • 1st Edition
    • 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.

  • Advances in Medical Imaging

    From Behavioral Analysis to Disease Prevention and Rehabilitation
    • 1st Edition
    • Dilber Uzun Ozsahin + 4 more
    • English
    Advances in Medical Imaging: From Behavioral Analysis to Disease Prevention and Rehabilitation presents a comprehensive exploration of the rapidly evolving field of medical imaging at the crossroads of clinical science, behavioral research, and public health. This book addresses the growing need to understand how advanced imaging technologies—such as MRI, PET, and ultrasound—are transforming healthcare beyond traditional diagnosis. By capturing structural, functional, metabolic, and molecular processes, these innovations provide new insights into brain function, lifestyle impacts, chronic disease, and rehabilitation, supporting the shift toward personalized and preventive medicine.The book is organized into five thematic sections covering a wide range of topics. Early chapters focus on the integration of neuroimaging with behavioral science, highlighting brain–behavior relationships through cutting-edge imaging modalities and cognitive software. Subsequent sections examine the effects of sedentary lifestyles on brain health, vascular function assessed by ultrasound, and musculoskeletal aging through imaging biomarkers. Additional chapters explore chronic disease management, yoga and cognitive well-being, stroke recovery, traumatic brain injury rehabilitation, and the expanding role of imaging in oncology and systemic diseases. Throughout, the book emphasizes the use of artificial intelligence, radiomics, and multimodal imaging to enhance precision medicine and population health research.Advances in Medical Imaging: From Behavioral Analysis to Disease Prevention and Rehabilitation offers valuable insights for researchers, clinicians, and healthcare professionals interested in the applications of medical imaging across multiple disciplines. It highlights imaging’s critical role in advancing personalized care, supporting preventive strategies, and fostering innovation in modern healthcare.

  • Intelligent Semantic Analysis for Healthcare

    • 1st Edition
    • Sonali Vyas + 3 more
    • English
    Intelligent Semantic Analysis for Healthcare explores the latest trends, developments, and future directions of intelligent semantic analysis techniques on retrieving and managing meaningful medical information for healthcare information systems. The book explores different computational methods, ideas, strategies, and techniques to analyze relevant healthcare information in an innovative and efficient way, thus bridging the gap between gathering and comprehending data with healthcare and biological applications. It offers a comprehensive view of intelligent semantic analysis in healthcare, bridging the gap between data collection and healthcare applications, and providing innovative computational methods for data analysis.Sections focus on intelligent semantic analysis rather than broader topics of big data and healthcare analytics. Additionally, the book is geared towards practical approaches and innovative techniques for state-of-the-art and current challenges in healthcare data management.

  • Symbiotic Planning for Urban Futures

    A Paradigm for Human-AI Co-Creation
    • 1st Edition
    • Zhong-Ren Peng
    • English
    Symbiotic Planning for Urban Futures: A Paradigm for Human-AI Co-Creation presents a framework for harnessing artificial intelligence in urban planning while preserving democratic control over urban futures. The book establishes symbiotic planning as a falsifiable paradigm grounded in five technology-neutral axioms and operationalized through governed friction, in which AI acts as a governed co-creator across the CORE framework: Collaboration, Options, Refinement, and Execution. It shows how planners can guide AI systems to generate options, support evidence-based refinement, translate values into constraints, and communicate context and equity considerations to decision-makers. By treating equity as a primary design constraint, the book offers a practical foundation for more transparent, accountable, and democratic urban governance in an algorithmic age.This book will be of value to urban planners, civic technologists, policymakers, researchers, and students seeking to understand how AI can support public-sector decision-making without displacing human judgment or public accountability.

  • Neuro-Symbolic AI

    Integrating Neural Networks and Symbolic Reasoning
    • 1st Edition
    • Sarika Jain + 3 more
    • English
    Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning explores the convergence of two historically distinct paradigms in artificial intelligence—data-dr... neural networks and logic-based symbolic reasoning. This book presents a comprehensive roadmap of this emerging hybrid discipline, offering deep theoretical insights, practical methodologies, and transformative applications across diverse research sectors, including healthcare, finance, engineering, and autonomous systems. It is structured into four parts—Foundational Principles, Hybrid Models and Techniques, Real-World Applications, and Emerging Challenges, bringing together cutting-edge research and expert perspectives to highlight how Neuro-Symbolic AI enhances interpretability, reasoning capabilities, and trust in intelligent systems.While neural networks have achieved remarkable success in perception and pattern recognition tasks, they often lack the reasoning, transparency, and generalizability that symbolic systems excel at. Conversely, symbolic AI lacks the flexibility and scalability of deep learning. This handbook directly addresses these challenges by providing a structured approach to Neuro-symbolic AI, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques (e.g., knowledge graphs, compositionality, category theory), and diverse real-world applications. This book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume.

  • Generative Artificial Intelligence for Neuroimaging

    Methods and Applications
    • 1st Edition
    • Deepika Koundal + 1 more
    • English
    Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.

  • Artificial Intelligence in Brain Disorders

    Innovations in Diagnosis and Treatment
    • 1st Edition
    • Pranav Kumar Prabhakar + 3 more
    • English
    Artificial Intelligence in Brain Disorders: Innovations in Diagnosis and Treatment focuses on the utilization of AI and machine learning to enhance current practices in the diagnosis and treatment of neurological disorders. Each chapter provides in-depth exploration of specific areas where AI can improve existing methodologies, offering practical guidance, case studies, and research findings that can be directly applied in the field. It explains the application of AI in diagnosing and treating major neurological illnesses and showcases the potential of AI in predicting diseases such as epilepsy and neurodegenerative disorders.As such, this book offers a detailed overview of AI and machine learning techniques relevant to neurological research.

  • Usability Testing Essentials

    Ready, Set ...Test!
    • 3rd Edition
    • Carol M. Barnum
    • English
    Usability Testing Essentials: Ready, Set ...Test!, Third Edition presents a practical, step-by-step approach to learning the entire process of planning and conducting a usability test. The book explains how to analyze and apply results and what to do when confronted with budgetary and time restrictions. This is the ideal book for anyone involved in usability or user-centered design—from students to seasoned professionals. Updated throughout, this book reflects the latest approaches, tools, and techniques needed to begin usability testing or to advance in this area.

  • 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.

  • Explainable AI for Transparent and Trustworthy Medical Decision Support

    • 1st Edition
    • Abhishek Kumar + 4 more
    • English
    Explainable AI for Transparent and Trustworthy Medical Decision Support equips readers with a comprehensive and timely resource that presents the principles, methodologies, and real-world applications of explainable AI (XAI) within the medical context. Covering a wide range of use cases—from radiology and pathology to genomics and clinical decision support systems—the book provides in-depth discussions on how XAI techniques can enhance interpretability, improve clinician trust, meet regulatory requirements, and ultimately lead to better patient outcomes. The book demystifies the workings of machine learning models and highlights techniques that make them interpretable.It is designed to empower not only AI researchers and developers but also healthcare administrators and policymakers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The book's authors bring together theory, implementation strategies, ethical implications, and case studies under one cover, offering a multidisciplinary perspective that aligns computer science with medical practice and healthcare policy.

  • AI-Driven Cybersecurity for Intelligent Healthcare Systems

    • 1st Edition
    • Balamurugan Balusamy + 3 more
    • English
    AI-Driven Cybersecurity for Intelligent Healthcare Systems explores the intersection between AI, cybersecurity, and healthcare. The book offers detailed insights into the unique cybersecurity challenges faced by the healthcare sector and the role of AI in addressing these challenges. It presents case studies and real-world applications to illustrate the effectiveness of these solutions and highlights the significance of data privacy in healthcare and methods to ensure secure data sharing and storage. Topics such as federated learning, homomorphic encryption, and blockchain technology are covered to demonstrate how AI can enhance data security without compromising patient privacy.This book will be an essential resource for anyone involved in the healthcare industry, offering practical solutions and fostering a more in-depth understanding of how AI can revolutionize cybersecurity in healthcare.

  • AI-Powered Developments in Medical Robotics

    Data-Driven Techniques for Enhanced Surgical Efficiency
    • 1st Edition
    • Thomas Heinrich Musiolik + 3 more
    • English
    AI-Powered Developments in Medical Robotics: Data-Driven Techniques for Enhanced Surgical Efficiency offers a comprehensive exploration of AI-driven innovations, robotics, and data-driven techniques specifically tailored for medical applications. The book strikes a balance by addressing foundational principles, emerging technologies, and their practical implementation in real-world scenarios. It enhances its value through the inclusion of real-world case studies and interdisciplinary perspectives, making it relevant for professionals, researchers, and students alike. The book explores future developments, such as augmented and virtual reality in medical robotics, positioning itself as a forward-thinking resource.By addressing current gaps in the field, including regulatory challenges, training needs, and cost-effectiveness, the book ensures a well-rounded approach that appeals to both advanced and emerging markets. This multifaceted perspective enriches the reader's understanding and equips them with actionable insights for navigating the complexities of AI-driven healthcare robotics. The book serves as a definitive reference for a global audience seeking innovation and practical solutions in the rapidly evolving landscape of medical technology, bridging the gap between theory and practice in a critical area of healthcare advancement.

  • Grey Wolf Optimizer

    A Pack of Solutions for Your Optimization Problems
    • 1st Edition
    • Seyedali Mirjalili
    • English
    Grey Wolf Optimizer: A Pack of Solutions for Your Optimization Problems offers in-depth coverage of recent theoretical advancements in GWO, as well as several variants, improvements, and hybrid approaches developed to enhance the GWO's performance and adaptability. The use of generative AI to improve this algorithm and make it more generic is also explored, along with diverse applications across multiple fields to illustrate the practical utility and versatility of the methods presented. The book offers a deep dive into the algorithm's foundations and presents new developments to help researchers overcome common challenges.It features numerous case studies and real-world examples across various fields, such as engineering, healthcare, finance, and environmental management. These applications demonstrate the versatility and effectiveness of the GWO in addressing complex, interdisciplinary challenges, making the content highly relevant and practical for readers. Written by some of the world’s most highly cited researchers in the field of artificial intelligence, algorithms, and machine learning, the book serves as an essential resource for researchers and practitioners interested in applying and developing the Grey Wolf Optimizer.

  • Cyber Threat Intelligence for Cloud Systems

    A Comprehensive Guide to Becoming an Analyst
    • 1st Edition
    • Gianni D'Angelo
    • English
    Cyber Threat Intelligence for Cloud Systems: A Comprehensive Guide to Becoming an Analyst begins with the foundational study of asset vulnerabilities, laying the groundwork for understanding the weak points within cloud environments. It then introduces tools and methodologies for the automated collection and supervision of data from assets, culminating in the exploration of advanced techniques, such as Artificial Intelligence and Machine Learning for data analysis and automated threat response. Alongside theoretical concepts, the book includes practical examples, hands-on exercises in Python, existing tools, and real-world case studies to help readers solidify their understanding and apply what they’ve learned.Cybersecurit... is one of the fastest-growing fields in today’s technological landscape, and with the increasing complexity and pervasiveness of cyber threats, especially in cloud environments, the need for qualified professionals such as Cyber Threat Analysts has never been more critical. Here, author Gianni D’Angelo provides the technical knowledge required to analyze and defend digital infrastructures, covering both traditional and modern approaches, highlighting how CTI supports decision-making and proactive defense strategies.

  • Essential Kubeflow

    Engineering ML Workflows on Kubernetes
    • 1st Edition
    • Prashanth Josyula + 2 more
    • English
    Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides solutions for real-world challenges.With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

  • Machine Learning Made Visual with Python

    • 1st Edition
    • Weisheng Jiang
    • English
    Machine Learning Made Visual with Python makes machine learning intuitive through Python coding and dynamic visualizations. The book helps readers grasp complex math concepts by showing how algorithms evolve step-by-step. Readers will learn how to develop a hands-on, visual, and practical path to mastering core machine learning algorithms. Importantly, the book includes practical examples and coding exercises.

  • Autonomous Vehicle Safety Solutions

    Foundations, Technologies, and Prospects for the Future
    • 1st Edition
    • Aparna Kumari
    • English
    Autonomous Vehicle Safety Solutions: Foundations, Technologies, and Prospects for the Future addresses the dynamically evolving state of the art that traverses and brings together automotive engineering, electronics engineering, and computer science. Its technical overview of smart vehicles' capabilities, propelled by headway in artificial intelligence and sensor technologies, enables in-depth understanding of the safety-assurance complexities that guide their design and operation.The comprehensive volume distinguishes itself for its treatment of several exciting, emerging opportunities and trends, including machine learning algorithms, V2X connectivity, and cybersecurity. Up-to-date applications are featured through practical examples and expert insights, bringing additional value to a discourse which underscores the critical need for ongoing safety advancements and adaptability in the fast-moving context of autonomous driving systems, with their related implications on sustainability and society as a whole.The outcome is a cornerstone resource for academia and industry alike that encourages further transformative, interdisciplinary investigations to bring safety solutions to maturity and subsequent rigorous testing for validation standards to be defined, thus realizing, in a future not distant from now, a scenario where self-driving vehicles can co-exist seamlessly with traditional modes of transportation and also integrate reliably, efficiently, and without risks within the larger infrastructure.

  • Deep Learning in Bioinformatics

    Techniques and Applications in Practice
    • 2nd Edition
    • Habib Izadkhah
    • English
    Deep Learning in Bioinformatics: Techniques and Applications in Practice, Second Edition explores how deep learning can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. This updated edition includes several new chapters, applications, and examples for new Deep Learning advances and techniques.Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.

  • Encyclopedia of Multi-Attribute Decision Making (MADM)

    • 1st Edition
    • Gholamreza Haseli + 2 more
    • English
    Encyclopedia of Multi-Attribute Decision Making (MADM) presents current methods in MADM in a simple way, including Sections on Weighting Methods, Extensions for the MADM Methods, Ranking Methods, and Outranking Methods. Each method chapter presents two numerical examples for each method, one simple example with less than six criteria and six alternatives, and one complex example with six or more criteria and six or more alternatives. In addition, most chapters are written by the original developers of the method, ensuring insight into and practical application of MADM. The book is also filled with over 200 full-color figures that illustrate methods and applications.The book, in one volume, demystifies the complex world of MADM, blending theoretical concepts with hands-on practices and case studies. It bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of MADM.

  • Green Intrusion Detection Systems for IoT

    • 1st Edition
    • Saeid Jamshidi + 3 more
    • English
    Green Intrusion Detection Systems for IoT tackles the pressing security challenges posed by the rapid expansion of the Internet of Things (IoT). The book delves into innovative, lightweight security models and energy-aware IDS mechanisms that strike a balance between security efficacy, computational efficiency, and environmental sustainability. Sections discuss the transformative role of IoT and the need for sustainable security solutions, highlight the distinctions between traditional and Green IDS, focus on lightweight security models essential for resource-constrained IoT devices, and delve into energy-efficient network designs.Additional sections explore green IDS mechanisms, including machine learning and distributed approaches, IoT vulnerabilities and mitigation strategies, practical examples of sustainable IDS in various smart environments, real-world case studies, and future directions in sustainable IoT security. The book concludes with actionable recommendations that align technological advancements with global sustainability goals.

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