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Elsevier

Books in Artificial intelligence general

  • 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.

  • Principles of Medical Biohybrid Microrobots

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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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-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.

  • 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.

  • Distributed AI in the Modern World

    Technical and Social Aspects of Interacting Intelligent Agents
    • 1st Edition
    • Andrei Olaru + 3 more
    • English
    Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents presents state-of-the-art insights into the various forms of distribution of artificial intelligence, with practical application instances. Sections provide readers with practical solutions at an architectural level, with solutions presented on the distribution of the learning process and the utilization of machine learning models in a distributed system, tools that enable the distribution and interaction of artificial learning entities, how multi-agent systems and machine learning can be combined, the physical embodiment of intelligent agents, and the interaction of intelligent computing units bound to physical space.Following sections emphasize the challenges that are common to all scenarios and solutions that apply in a wider range of cases. This book does not analyze the internal workings of machine learning models (for instance, in the case of multi-agent reinforcement learning), but instead provides readers with an overview of the challenges brought by the need of artificially intelligent entities to interact with other entities and with their environments, along with practical solutions at an architectural level.

  • Understanding Models Developed with AI

    Including Applications with Python and MATLAB Code
    • 1st Edition
    • Ömer Faruk Ertuğrul + 2 more
    • English
    Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need to build AI models, along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems.As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable.

  • Intelligent Cyber-Physical Systems for Sustainable Healthcare

    • 1st Edition
    • Vandana Bajaj + 5 more
    • English
    Intelligent Cyber-Physical Systems for Sustainable Healthcare addresses the integration of emerging technologies like Virtual Reality, Smart Robotics, and Human-Computer Interaction, which are crucial for maximizing digital health's potential. The book includes case studies that highlight challenges faced by medical practitioners and industry professionals, providing valuable insights for designing sustainable iCPS solutions. Aimed at biomedical engineers, researchers, and industry professionals, it offers guidance for developing practical applications in sustainable healthcare. Additionally, shared, cross-disciplinary experiences assist engineers with less clinical expertise in enhancing healthcare applications in the intelligent digital health sector.Overall, case studies illustrate past challenges in the healthcare industry and present effective solutions to overcome them, making the book a comprehensive resource for advancing sustainable healthcare practices.

  • Federated Learning

    Foundations and Applications
    • 1st Edition
    • Rajkumar Buyya + 2 more
    • English
    Federated Learning: Foundations and Applications provides a comprehensive guide to the foundations, architectures, systems, security, privacy, and applications of federated learning. Federated learning has become an increasingly important machine learning technique because it introduces local data analysis within clients and requires exchanging only model parameters between clients and servers. This book covers the fundamental concepts of federated learning, including machine learning, deep learning, centralized learning, and distributed learning processes. The book then progresses to cover the architectures, algorithms, and system models of federated learning, as well as security, privacy, and energy-efficiency techniques. Finally, the book presents various applications of federated learning through real-world case studies illustrating both centralized and decentralized federated learning.

  • GeoAI for Earth Observation Imagery

    Fundamentals and Practical Applications
    • 1st Edition
    • Dalton Lunga + 1 more
    • English
    GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications comprehensively covers methodologies of AI and Machine Learning applications of image processing for Earth Observation (EO) Imagery. As traditional image processing methods face challenges with handling vast volumes of EO imagery, leading to efficiencies and limitations when extracting meaningful insights, AI-driven approaches can enhance the efficiency, accuracy, and scalability of image processing. Chapters cover essential methodologies including atmospheric compensation, image enhancement techniques like deblurring and superresolution, and advanced analysis methods such as semantic segmentation and object detection.Cutting-ed... approaches to computing, automating, and optimizing image processing tasks are also covered. Additionally, emerging trends in GeoAi and their implication on future research are reviewed. The book serves as an essential guide for navigating the complexities of spatial data and equips readers with knowledge to enhance their analytical capabilities.

  • AI and Data Science in Medical Research

    • 1st Edition
    • Olfa Boubaker
    • English
    AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies.The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

  • Artificial Intelligence Applications in Emerging Healthcare Technologies

    • 1st Edition
    • Miguel Antonio Wister Ovando + 2 more
    • English
    Artificial Intelligence Applications in Emerging Healthcare Technologies presents the latest advances and state-of-the-art methods and applications of computer science and emerging AI technologies in health and medicine. The book explores the impact of artificial intelligence (AI) in healthcare for medical decision-making and data analysis, tackling topics such as cloud computing, cybersecurity, the internet of things, natural language processing, virtual health, data science applied to healthcare, personalized medicine, imaging, diagnosis, drug discovery, and diseases, among others.Chapters present adaptations or improvements on previous models and algorithms to process data from different sources. Other chapters investigate new formulations for the optimization of known procedures and algorithms. Finally, all chapters use experimental methods to study problems of interest in healthcare. This is a great resource for researchers and students who want to learn how machine learning algorithms and other data science techniques have been implemented to solve healthcare-related problems.

  • 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 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 are explored in-depth, equipping readers with the tools and knowledge needed to excel in the rapidly evolving landscape of supply chain management. Readers will learn how 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, understand the tools, technologies, and strategies essential for a successful transformation, evaluate the importance of end-to-end supply chain visibility, employ methods and technologies to enhance this visibility in practical scenarios, and apply system dynamics modeling techniques to address complex supply chain problems to optimize supply chain processes, and much more.

  • Cybersecurity for Healthcare Systems in the Internet of Medical Things Era

    • 1st Edition
    • Janmenjoy Nayak + 2 more
    • English
    Cybersecurity for Healthcare Systems in the Internet of Medical Things Era is driven by the imperative to address the intricate convergence of healthcare, technology, and security. In response to the burgeoning challenges presented by the Internet of Medical Things (IoMT), this book is designed to be an indispensable resource for computer systems experts, healthcare staff, and executives. It goes beyond the surface, offering strategic insights and actionable strategies that encompass not only the IoMT landscape but also the intersection of artificial intelligence, signal processing, and cyber security. Cybersecurity for Healthcare Systems in the IoMT Era serves as a problem-solving compass for a diverse readership in the healthcare landscape. For healthcare professionals and IT leaders, the book untangles the complexities of integrating and securing Internet of Medical Things (IoMT) devices, offering a roadmap for understanding and navigating this rapidly evolving terrain. Biomedical engineers, burdened with strategic and everyday decisions, find in this book strategic insights and actionable strategies, empowering them to make informed choices amidst the ever-changing challenges posed by technology and cybersecurity threats. The authors present a comprehensive guide that not only elucidates the challenges and opportunities presented by IoMT but also explores how the synergy of AI and signal processing can elevate healthcare systems. This integration is crucial in deciphering the intricate nuances of medical data, enhancing diagnostics, and fortifying the security of interconnected healthcare networks.

  • AI Platforms as Global Governance for the Health Ecosystem

    The Future's Global Hospital
    • 1st Edition
    • Dominique J. Monlezun
    • English
    AI Platforms as Global Governance for the Health Ecosystem: The Future’s Global Hospital provides comprehensive and actionable approaches for readers to understand and optimize responsible AI to create global governance for the healthcare ecosystem. The book explores how AI platforms can transform hospitals and clinical practice by digitally unifying patients, providers, and payors, advancing healthcare for all. Users will find content that defines and explains the main hurdles and technical innovations in responsibly governing AI platforms for efficient, equitable, and sustainable global healthcare.Additiona... sections delve into the history, science, politics, economics, ethics, policy, and future of these AI platforms, and how governance efforts can work toward the common good. Written from the first-hand perspective of a practicing physician-data scientist and AI ethicist, the book maps out how to develop successful governance for AI platforms.

  • The AI Ideal

    AIdealism and the Governance of AI
    • 1st Edition
    • Niklas Lidströmer
    • English
    The AI Ideal: Aidealism and the Governance of AI 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, the book argues for a new global framework—one where AI serves justice, enlightenment, and human betterment. Rooted in European Enlightenment ideals, Scandinavian social models 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. Instead of a warning of the catastrophe of AI, Dr. Lidströmer offers an actionable vision for ensuring AI strengthens democracy, ethics, and human dignity. The book explicitly gives a manifesto for practical action, including a plan for how to harness and use AI for the common good so that it benefits everyone, not just the few. It 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. As the risks are real and the threats are mounting, sections cover how AI could empower autocrats, disrupt economies, and undermine human agency while also highlighting how AI could also be our greatest tool for wisdom, fairness, and progress—if governed with foresight and courage.

  • Deep Learning Applications in Neuroinformatics

    Advances, Methods, and Perspectives
    • 1st Edition
    • Karthik Ramamurthy
    • English
    Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts.

  • Harnessing Artificial Intelligence to Ensure Diverse Global Teams

    • 1st Edition
    • Harish Garg + 3 more
    • English
    Harnessing Artificial Intelligence to Ensure Diverse Global Teams explores new research and applications of AI which can be used to address the distinct challenges of diverse, distributed teams. Incorporating compelling case studies and strategic guidance, the book demonstrates how AI can be developed and applied within systems and programs to promote inclusion, break down barriers, and enhance collaboration in cross-cultural organizational settings. Providing case studies and examples, this book equips computer scientists and engineers with actionable strategies for integrating AI seamlessly into programs and applications designed for diverse global teams. With contributions from experts in AI and team dynamics, this book will benefit leaders and team members seeking to leverage AI for improved teamwork across geographical and cultural boundaries, providing an up-to-date resource for uniting, uplifting, and optimizing global collaborations through inclusive artificial intelligence.

  • Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics

    • 1st Edition
    • Sayooj Aby Jose + 2 more
    • English
    Mathematical Modeling and AI-Driven Computational Techniques for Epidemiology and Disease Dynamics offers a comprehensive exploration of innovative methodologies at the intersection of mathematics, biology, and medicine. This book delves into advanced mathematical modeling, artificial intelligence, and computational intelligence, providing essential tools for understanding and managing complex disease dynamics. Covering a wide range of topics, including fractional-order modeling, optimal control strategies, and privacy-preserving technologies, it addresses critical challenges in public health and healthcare systems. With contributions from leading experts, this volume bridges theoretical advancements and practical applications, making it an invaluable resource for researchers, healthcare professionals, and academics seeking interdisciplinary solutions to global health issues.

  • Digital Transformation in Artificial Systems

    Engineering Requirements and Political, Economic, and Philosophical Challenges
    • 1st Edition
    • Mirko Farina + 3 more
    • English
    The last decade has seen exponential growth in the development of digital technologies. This has led to significant shifts in the political arena as well as in the economy, precipitating a series of revolutionary changes in the fabric of our societies, which have had far-reaching consequences and effects on the way we relate and connect to each other. Digital Transformation in Artificial Systems: Engineering Requirements and Political, Economic, and Philosophical Challenges focuses on analyzing the engineering requirements as well as the political consequences, overarching the philosophical and ethical implications of this transformation, especially in relation to its application in artificial systems. In this context, the concept of digital transformation (understood as the practice of redefining models, functions, operations, processes, and activities by leveraging technological advancements to build efficient digital environments) has become increasingly important. This book brings together key concepts, ideas, and frameworks related to this idea. It promotes an inclusive and responsible digital transformation capable of addressing the constraints on the global digital divide, deepening cooperation in digitization, industrialization, and innovation, while furthering our understanding of the ethical and moral challenges associated with such a development. The distinctive and most original element of the book is its interdisciplinarity. It will allow readers to gather crucial insights that will be instrumental to better understand the reach of the forthcoming AI revolution, its multidimensionality, and its potential impact on people and society.

  • Connected Diagnoses

    IoT, Healthcare, and Digital Forensics
    • 1st Edition
    • Keshav Kaushik + 1 more
    • English
    Connected Diagnoses: IoT, Healthcare, and Digital Forensics investigates the complex intersection of IoT, healthcare, and digital forensics. The book explores the intricate relationships between these fields, with a focus on cybersecurity, patient data ethics, and challenges in IoT investigations. It advances knowledge on leveraging IoT securely to enhance patient care and digital forensic analysis, providing significant insights from experts along with practical guidance for those operating at the crossroads of these critical disciplines. The book helps professionals grasp, adapt to, and capitalize on the interconnected nature of emerging technologies to ensure ethics, security, and safety. Users will find this to be a comprehensive resource that benefits researchers and practitioners who seek to understand the convergence of medical technology, interconnected devices, and digital forensics.

  • The Governance of Artificial Intelligence

    • 1st Edition
    • Tshilidzi Marwala
    • English
    The Governance of Artificial Intelligence provides an essential approach to AI governance, including proactive and comprehensive strategies that efficiently balance innovation and ethical concerns. The book prioritizes social welfare and upholds human rights by maximizing the benefits of AI while reducing its negative aspects. Sections address the principles that govern artificial intelligence, data-related topics, AI algorithms, the issue of computing, applications, and AI governance. Throughout each section, the idea that it is essential to implement a versatile governance structure that incorporates several fields of study and encourages diversity is reinforced. Additionally, utilizing existing regulatory frameworks, ethical standards, and industry benchmarks is essential. Moreover, the book maintains that 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. Because of 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, it is imperative to cultivate global cooperation in regulating AI as 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.

  • AI and Data Science in Healthcare 5.0

    • 1st Edition
    • Olfa Boubaker
    • English
    AI and Data Science in Healthcare 5.0 delves into the innovative developments in Healthcare 5.0, focusing on smart medical robots, devices, and connected hospitals. It explores the role of robotics in modern healthcare, including advanced wearables enhanced by deep learning. The volume also addresses the integration of blockchain technology, IoT, and cloud computing in healthcare, emphasizing real-time applications in precision healthcare. The discussion extends to energy optimization in smart hospitals, federated learning for IoMT networks, and advanced patient monitoring systems.The volume concludes with an overview of the future opportunities and challenges in Healthcare 5.0.

  • Challenges and Applications of Generative Large Language Models

    • 1st Edition
    • Anitha S. Pillai + 2 more
    • English
    Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

  • Multilevel Quantum Metaheuristics

    Applications in Data Exploration
    • 1st Edition
    • Siddhartha Bhattacharyya + 4 more
    • English
    Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.

  • 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.

  • Engineering Generative AI-Based Software

    • 1st Edition
    • Miroslaw Staroń
    • English
    Engineering Generative-AI Based Software discusses both the process of developing this kind of AI-based software and its architectures, combining theory with practice. Sections review the most relevant models and technologies, detail software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, explore various architectural styles and tactics for such systems, including different programming platforms, and show how to create robust licensing models. Finally, readers learn how to manage data, both during training and when generating new data, and how to use generated data and user feedback to constantly evolve generative AI-based software.As generative AI software is gaining popularity thanks to such models as GPT-4 or Llama, this is a welcomed resource on the topics explored. With these systems becoming increasingly important, Software Engineering Professionals will need to know how to overcome challenges in incorporating GAI into the products and programs they develop.

  • Multimodal Learning Using Heterogeneous Data

    • 1st Edition
    • Saeid Eslamian + 3 more
    • English
    Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

  • Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction

    • 1st Edition
    • Shubham Mahajan + 2 more
    • English
    Intelligent Systems for Neurocognition and Human-Robot-Computer Interaction explores the transformative potential of intelligent systems in enhancing human well-being through advancements in neurocognitive technologies, robotics, and AI-driven healthcare. This book addresses the fragmented knowledge at the intersection of these fields, enabling readers to understand the latest advancements and discover practical strategies for implementation. It highlights the importance of ethical considerations and governance in deploying AI and robotics in healthcare settings. Each chapter covers various aspects, including personalized medicine, human-robot interaction, cognitive robotics, wearable technologies, and the role of AI in mental health. The book also discusses the implications of cloud computing, big data, and human-computer interaction in enhancing cognitive and emotional well-being.

  • Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices

    • 1st Edition
    • Aditya Khamparia + 1 more
    • English
    Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices focuses on the intersection of biometric-driven computational approaches and techniques within a connected multi-modal environment, particularly emphasizing their applications in healthcare. The book explores cutting-edge methodological approaches that leverage technologies like blockchain and integrate them with information fusion, data security for medical devices, and trust management. Readers with find this to be a comprehensive overview of the topics covered, including machine learning and deep learning for biomedical-based biometrics, computational medical imaging techniques, security strategies for healthcare systems, AI technology for multimodal biometrics, and feature reduction techniques.Other sections cover blockchain and fog computing models for medical sensor data storage and evolutionary optimization for biometric feature identification and recognition, amongst others.

  • Artificial Intelligence for the Water-Energy-Food Nexus

    • 1st Edition
    • Shahryar Jafarinejad + 1 more
    • English
    Artificial Intelligence for the Water-Energy-Food Nexus considers the interrelationships among water, energy, and food, which are key to sustainable development, and the potential of artificial intelligence (AI) as a useful tool to facilitate research and development within and between these areas as well as Water-Energy-Food (WEF) nexus.The first chapter introduces the WEF nexus concepts, assessment/analysis methodologies and tools, challenges, trends, and future perspectives. The second chapter summarizes AI applications to the water sector/industry with a focus on the importance, general applications and real-world applications, as well as challenges and future perspectives. The third chapter focuses on the application of AI techniques to the water and wastewater treatment systems including treatment processes, urban drinking water systems, and integrated urban drainage systems. The fourth chapter explores the application of AI techniques to the non-renewable and renewable energy systems, load monitoring, load demand forecasting, smart grids, energy optimization and process control, energy storage systems, and case studies and real-world applications as well as challenges in AI application to the energy industry and future directions. The application of AI techniques in the food industry in areas of organoleptic properties, nutrition, toxicology, and food chemistry, biotechnology processes and microbiology security, food processing and manufacturing, conservation and storage conditions, supply chain, markets, and distribution, and food industry environments (circular economy, water, and energy management) as well as challenges and opportunities are discussed in the fifth chapter. The final chapter reviews/presents the application of AI techniques to address problems associated with the WEF nexus.This book can be of interest to all those who are looking to understand and utilize AI techniques in the context of water, energy, food, and WEF nexus, including researchers, advanced students, faculty, engineers, R&D, industry professionals, and policymakers.

  • Data-Driven Diagnostics and Disease Prediction with AI Optimization

    • 1st Edition
    • Shailendra Pratap Singh + 3 more
    • English
    Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources.

  • Federated Learning in Metaverse Healthcare

    Personalized Medicine and Wellness
    • 1st Edition
    • Shubham Mahajan + 1 more
    • English
    Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. The book introduces federated learning, emphasizing its advantages over traditional centralized machine learning in healthcare. It provides a historical context and discusses the technological advancements that led to the emergence of metaverse healthcare. Privacy-preserving methods crucial for protecting sensitive healthcare data within federated learning environments are also examined, underscoring the importance of secure communication protocols. Other important points include the transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences.The role of telemedicine in facilitating remote diagnostics and consultations via virtual platforms, and the applications of augmented reality wearables for real-time health monitoring and wellness tracking are detailed. Additionally, the book discusses federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, its role in predictive modeling for disease risks and prevention, as well as virtual health coaches offering personalized guidance for wellness management. The challenges and ethical dilemmas of metaverse healthcare and federated learning, along with potential solutions, are also considered.

  • 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.

  • Applied Mathematical Modeling for Biomedical Robotics and Wearable Devices

    • 1st Edition
    • S. Sountharrajan + 3 more
    • English
    Applied Mathematical Modelling for Biomedical Robotics and Wearable Devices delves into the innovative convergence of mathematical frameworks and biomedical engineering. The book begins by exploring how advanced mathematical modelling underpins the development and optimization of robotic systems and wearable technologies tailored for medical applications. With a strong emphasis on practical implementation, it serves as a bridge between theoretical concepts and real-world engineering challenges in the healthcare sector. Readers will gain insights into the transformative role of mathematical techniques that drive precision, functionality, and human-centric design in cutting-edge medical technologies.The book also covers interdisciplinary applications, integrating domains like biomechanics, sensor technology, and data analytics. By highlighting case studies and real-world scenarios, it showcases practical advancements in wearable devices that monitor health metrics and robotic systems that assist in surgical procedures.

  • Minds, Machines, and Misinformation

    Decoding Bias, Algorithms, and Trust
    • 1st Edition
    • Don Donghee Shin
    • English
    Algorithms have become the key organizer through which power is enacted in our society. A huge amount of data regarding our daily routines are monitored and analyzed to make recommendations that manage, control, and lead our behaviors in everyday life. AI, Humans, and Misinformation: How Does AI Alter Human Behavior and How Do Humans Influence Algorithmic Misinformation? is a guide to understanding the dynamics of AI and misinformation in human contexts by addressing meaningful questions—How does AI alter human behavior and how do humans influence algorithmic decision-making? In answering these questions, this book examines the role of misinformation, disinformation, and fake news, and shows readers how to develop AI methods and algorithms that combat misinformation by using AI design choices that provide users and developers alike with meaningful control over AI. This book brings together various perspectives on algorithms into an integrated conceptual framework, and provides a broad socio-technical analysis, addressing critical and ethical issues of misinformation and fake news. The book offers a compelling insight into the misinformation phenomenon and the future of AI-based society. Readers will find an integrated technical analysis of the logic and social implications of algorithmic processes. Reporting from the cutting edge of critical technical methods and research, the result is useful and constructive for developing the relations between algorithms and humans. This is an imperative methodology for understanding what is at stake as industry and government use AI to reshape the world.