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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.

  • Smart Healthcare 2.0

    Integrating Digital Twins with AI-Driven Predictive Analytics
    • 1st Edition
    • Ramesh Chandra Poonia + 1 more
    • English
    Smart Healthcare 2.0: Integrating Digital Twins with AI-Driven Predictive Analytics offers a ground-breaking exploration of how digital twin technology, combined with real-time sensing and predictive analytics, is transforming healthcare delivery. As the global healthcare landscape shifts toward proactive, personalized care, this book addresses the urgent need for comprehensive resources that unify artificial intelligence, Internet of Things (IoT), and biomedical engineering within the digital twin framework. It provides an essential guide for researchers, engineers, and clinicians aiming to harness virtual patient models and data-driven insights to improve health outcomes and system efficiency in the era of ubiquitous healthcare.This volume covers a wide spectrum of topics, starting with foundational concepts of digital twins in precision health and advancing through smart sensing technologies, scalable system architectures, and AI-powered predictive analytics. Readers will explore detailed discussions on edge-cloud computing, secure communication protocols including blockchain, and simulation platforms that enable virtual patient modeling. The book also addresses critical themes such as chronic disease management, emergency response optimization, ethical AI deployment, interoperability standards, and workforce readiness. Real-world case studies and future-focused chapters on cognitive twins and quantum simulation provide a rich, multidisciplinary perspective. Each chapter is complemented by pedagogical tools and supported by a companion website offering extended resources for teaching and applied research. Researchers and academics will find a consolidated, interdisciplinary framework linking theory with practical healthcare applications, ideal for advancing scholarship and innovation.Biomedica... and clinical engineers gain actionable insights into system design, sensor integration, and clinical validation for building reliable, patient-centered solutions. Healthcare AI engineers and data scientists will benefit from specialized guidance on deploying predictive models, managing multi-sensor data fusion, and ensuring privacy-compliant, real-time analytics. This book empowers stakeholders across the healthcare ecosystem to drive the next generation of intelligent, adaptive, and trustworthy digital health systems.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Symbiotic Planning for Urban Futures

    Empowering Collaborative Human-AI Planning
    • 1st Edition
    • Zhong-Ren Peng
    • English
    AI-Driven Urban Planning: Shaping the Future of Cities presents a comprehensive guide to the transformative potential of artificial intelligence in urban planning. This book equips readers with the knowledge to harness data, analytics, and AI for creating sustainable, equitable, and livable urban environments. Exploring diverse applications—from understanding human mobility patterns to enhancing disaster response strategies and optimizing design processes—the book offers practical projects and illustrates how AI is shaping contemporary urban landscapes. By addressing both theoretical and practical dimensions, this resource aims to empower students, professionals, and policymakers with a holistic understanding of Urban Planning AI.It is organized into five parts, each tackling crucial aspects of Urban Planning AI. It first introduces core concepts, types, mechanisms, and ethical considerations surrounding AI. Part II then discusses the history of computer applications in urban and regional planning. Part III focuses on AI Applications in Urban Planning, addressing critical domains such as transportation, environmental, housing, economic, participatory, and health and safety planning. Part IV tackles challenges and ethical considerations, emphasizing equity, transparency, and data-related issues. Lastly, Part V explores future pathways of urban planning AI, discussing current trends, future visions, and interdisciplinary approaches essential for effective governance and policymaking.

  • 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.

  • 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.

  • 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.

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