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Books in Computer science

The Computing collection presents a range of foundational and applied content across computer and data science, including fields such as Artificial Intelligence; Computational Modelling; Computer Networks, Computer Organization & Architecture, Computer Vision & Pattern Recognition, Data Management; Embedded Systems & Computer Engineering; HCI/User Interface Design; Information Security; Machine Learning; Network Security; Software Engineering.

  • Designing Technology for an Aging Population

    Towards Universal Design
    • 2nd Edition
    • Jeff Johnson + 1 more
    • English
    Designing User Interfaces for an Aging Population: Towards Universal Design, Second Edition explores the unique needs of older adults in today’s digital landscape. The authors examine this demographic’s wide-ranging sensory, cognitive, physical, and emotional characteristics, connecting each to the challenges and opportunities older users face with technology. Backed by hundreds of global research studies, the book provides actionable design guidelines to enhance satisfaction and usability for seniors. Updated to reflect the latest advances in AI, robotics, and speech recognition, it offers fresh examples and case studies to keep designers informed about emerging trends.Beyond demographics and design principles, the book highlights common pitfalls in technology that can reduce accessibility for older adults. It discusses strategies for involving seniors directly in research and design, ensuring their voices shape digital innovation. The authors emphasize that older users remain underserved and often overlooked in technology studies, urging designers to broaden their approach. By addressing these gaps, the book helps professionals create more inclusive interfaces that better serve a rapidly growing segment of the technology-using population.

  • 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.

  • 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.

  • 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.

  • 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.

  • Usability Testing Essentials

    Ready, Set ...Test!
    • 3rd Edition
    • Carol M. Barnum
    • English
    Usability Testing Essentials: Ready, Set ...Test! presents a practical, step-by-step approach to learning the entire process of planning and conducting a usability test. It explains how to analyze and apply the 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, Usability Testing Essentials, Third Edition reflects the latest approaches, tools and techniques needed to begin usability testing or to advance in this area.

  • Introduction to Statistical Machine Learning

    • 2nd Edition
    • Masashi Sugiyama + 1 more
    • English
    Machine learning allows computers to learn and discern patterns without being programmed. When Statistical techniques and machine learning are combined together, they are a powerful tool for analyzing various kinds of data in many computer science/engineering areas, including image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning, Second Edition provides a general introduction to machine learning that covers a wide range of topics concisely and will help readers bridge the gap between theory and practice. Parts 1 and 2 discuss the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part 3 and Part 4 explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Parts 5 and 6 provide an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged algorithms in a range of real-world applications drawn from research areas such as image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials. The algorithms developed in the book include Python program code to provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. The Second Edition also includes an all-new Part 6 on on Deep Learning, including chapters on Feedforward Neural Networks, Neural Networks with Image Data, Neural Networks with Sequential Data, learning from limited data, Representation Learning, Deep Generative Modeling, and Multimodal Learning.

  • 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. AI has revolutionized the medical field, powering intelligent systems that assist in diagnostics, treatment planning, risk prediction, and patient management. However, as these models grow increasingly complex, their "black-box" nature has raised critical concerns about trust, transparency, accountability, and clinical adoption. Medical professionals, researchers, and patients alike are now demanding systems that not only perform with high accuracy but also offer clear explanations behind their decisions. This has led to a pressing need for Explainable Artificial Intelligence (XAI) in healthcare—a field where every decision can significantly impact human life. This 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 policy makers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The 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. By reading this book, readers will gain a practical and theoretical understanding of XAI concepts, tools, and techniques tailored for medical decision support. It will help them design systems that are not only accurate and efficient but also interpretable, transparent, and aligned with human-centric values.

  • Knowledge Graphs and Large Language Models

    Current Approaches, Challenges, and Future Directions
    • 1st Edition
    • Sanju Tiwari + 3 more
    • English
    Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions explores the cutting-edge fusion of two powerful artificial intelligence technologies: Large Language Models (LLMs) and Knowledge Graphs (KGs). LLMs have revolutionized our daily lives with their ability to generate human-like text, perform text classification, translate languages, summarize content, and even code. Meanwhile, KGs have structured information for decades, enabling machine-processable data and inference between real-world entities. This book addresses the growing interest in combining LLMs and KGs to leverage their respective strengths, enhancing machines' ability to process, interpret, and generate information with context and accuracy.The book is structured to provide a comprehensive understanding of this emerging field. Chapter 1 introduces the synergy between LLMs and KGs, setting the stage for the subsequent chapters. Chapter 2 delves into the capabilities and challenges of LLMs, while Chapter 3 focuses on the structure, function, and significance of KGs. Chapter 4 presents a conceptual framework for bridging LLMs and KGs, followed by Chapter 5, which discusses techniques for their integration. Chapters 6 and 7 explore how LLMs can enhance KGs and vice versa. Chapter 8 showcases applications of LLM-KG synergy across various domains. Chapter 9 addresses ethical, social, and technical challenges, and Chapter 10 looks ahead to future innovations. The book concludes with Chapter 11, summarizing key insights and advancements in intelligent systems.Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions is an essential resource for graduate students, researchers, and professionals in computer science. It offers valuable insights for adopting LLMs, KGs, and their advanced applications in research and product development. By bridging the gap between these technologies, this book equips readers with the knowledge to drive innovation and enhance the capabilities of intelligent systems.

  • Digital Twins in the Smart Classroom

    Utilising Sensor Networks in the Educational Environment
    • 1st Edition
    • Tuan Anh Nguyen
    • English
    The smart classroom allows the incorporation of digital devices and learning software into the school environment, as well as sensor networks for tracking classroom processes, for data gathering, and to developed insights into the management of scholarly activity. Additionally, modern infrastructure within schools can involve the installation of advanced Information and communication technology (ICT): student-friendly data can be collected from wearable devices (personal digital assistants, iPads, iPods, smart watches, etc.) via wireless sensor networks; smart sensors can monitor the classroom environment, such as noise level, CO2 level, temperature, humidity, lecturers’ voice, and students/lecturers’ motion (by PIR - passive infrared sensors); near-field communication can manage door security, personnel management, transportations, and digital wallets. The data from these devices can be evaluated using digital twin computing to reduce the disparity between real school and virtual school, and to generate more varied datasets. School digital twin is bridge between the real and virtual school. School digital twin also is the mirror that reflects the real school into the virtual school. Digital Twins in the Smart Classroom: Utilising Sensor Networks in the Educational Environment explores virtual schools that have incorporated digital twin computing into the day to day running of the classroom. The book discusses the foundational concepts, practical applications, and future directions. It covers the various aspects of virtual school, such as the student's daily activities, intelligent wireless sensor networks, the privacy issues, and how the internet of things, artificial intelligence, machine learning, cloud computing, fog/edge-computing can enable a smarter, more efficient, more optimised classroom.

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