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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.

  • 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.

  • Principles of Medical Biohybrid Microrobots

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

  • 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.

  • 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 insight 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 and our place within it. 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, the book bridges abstract ideas with real-world phenomena and demonstrates the role of randomness and noise in predictive models and simulations, helping readers understand the limits of computational systems in mimicking real-world processes.

  • 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, characterized by pandemics, aging populations, and escalating chronic conditions. This 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 that meet real-world challenges. Comprising six comprehensive sections, the book moves from core AI applications in electronic health records, drug discovery, data management, and privacy, through cutting-edge big data analytics for precise 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. Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data serves as an essential resource for graduate students, researchers, and engineers in computer science, data science, and biomedical informatics. It bridges theory and practical application, offering interdisciplinary insights, foundational background, detailed case studies, and guidance on navigating the next generation of healthcare data systems. Whether for research or real-world innovation, readers gain the tools to design, analyze, and implement intelligent healthcare data solutions for a rapidly evolving digital era.

  • 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.

  • 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.

  • 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 AI's analytical power while preserving democratic control over urban futures. This book establishes symbiotic planning as a falsifiable paradigm—grounded in five technology-neutral axioms and operationalized through governed friction—where AI acts as governed co-creator across the CORE framework: Collaboration, Options, Refinement, Execution. It clarifies distinct roles: AI synthesizes evidence, generates non-obvious options, and stress-tests plans; planners steward assumptions and translate values into constraints; communities contest and refine constraints; and authorized decision-makers set ends and grant time-bound approvals. Equity is treated as a primary design constraint, with equity floors as binding guardrails.This book serves as essential resource for urban planners, civic technologists, policymakers, researchers, and students committed to democratic urban governance in an algorithmic age. It provides actionable governance tools, including Civic Evidence Dossiers, Authorization Forums, Equity Gates, and a 100-Day Starter Kit, ensuring AI remains transparent, contestable, and subject to renewal. Whether navigating AI procurement, studying algorithmic accountability, or organizing for transparent decision-making, this book empowers readers to make cities more resilient, equitable, and democratically co-governed.

  • 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.

  • 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. 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. The book explores the benefits of remote patient monitoring, particularly for underserved populations, and explores edge AI for localized, low-latency diagnostic processing. Real-time imaging diagnostics are highlighted, demonstrating how 6G supports rapid transfer and analysis of high-resolution medical images. It also addresses predictive analytics, detailing AI models that forecast diseases and the role of IoT devices and wearables in healthcare diagnostics. Concepts of smart hospitals and the integration of blockchain technology for security and data integrity are discussed. Ethical considerations and regulatory challenges are thoroughly examined, ensuring patient privacy and compliance. The book concludes with insights into future trends and emerging technologies in AI diagnostics, including quantum AI and next-generation sensors.

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