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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.

BooksJournals

    • Robotics for Intervention in Healthcare

      • 1st Edition
      • March 1, 2026
      • Françoise J Siepel
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 5 2 3 0
      • eBook
        9 7 8 0 4 4 3 2 1 5 2 5 4
      Robotics for Intervention in Healthcare: From Technology to Clinical Practice bridges the gap between deep-core robotic intervention technology and clinical aspects including the physicians and the clinicians working and gives insight in the importance of connectivity at early stage but especially address which aspects are important to speed up the innovation chain.

    • The AI Ideal

      • 1st Edition
      • April 1, 2026
      • Niklas Lidströmer
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 9 7 2 7
      • eBook
        9 7 8 0 4 4 3 4 4 9 7 3 4
      The AI Ideal: Aidealism and the Governance of AI, Dr. Niklas Lidströmer presents a bold alternative to the prevailing AI doom narratives. While nearly all other experts focus solely on warning of catastrophe, Dr Lidströmer also 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, he argues for a new global framework—one where AI serves justice, enlightenment, and human betterment. Rooted in European Enlightenment ideals, Scandinavian social model 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—if we act now. Yet Aidealism does not promise utopia. The risks are real. The threats are mounting. AI could empower autocrats, disrupt economies, and undermine human agency. But it could also be our greatest tool for wisdom, fairness, and progress—if governed with foresight and courage. This book explicitly gives a manifesto for practical action. An action plan for how to harness and use AI for the common good, so that it benefits us all, rather than the few. The book 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. It also tries to explain the core of AI for the general audience. It delves into a very broad range of areas, from philosophy to music, politics to ethics, and mathematics and physics to sociology and medicine. It tries to usher in an technological Enlightenment to save us from the threat of a malign and Machiavellian use of AI. This is not another AI dystopia, nor is it blind optimism. It is a manifesto for action—a call to use AI not just to enhance intelligence, but to make humanity nobler. The AI revolution is not something happening to us—it is something happening through us. The only question is: Will we build wisely? For those seeking a visionary, constructive, and ethically grounded roadmap for AI—one written in the spirit of true idealism—this book is essential reading.

    • Multilevel Quantum Metaheuristics

      • 1st Edition
      • January 1, 2026
      • Siddhartha Bhattacharyya + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 1 3 6 7
      • eBook
        9 7 8 0 4 4 3 3 3 1 3 7 4
      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.

    • Tactile Internet for a Human-Centred Metaverse

      • 1st Edition
      • January 1, 2026
      • Frank H. P. Fitzek + 5 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 0 4 4 8
      • eBook
        9 7 8 0 4 4 3 3 0 0 4 5 5
      Tactile Internet for a Human-Centred Metaverse is a groundbreaking, comprehensive guide to this innovative communication platform that merges the physical and digital worlds, transforming how we experience the Metaverse and interact with robots and other cyber-physical systems. From exploring real-world applications in healthcare, industry, education, and beyond, to delving into the cutting-edge components, sensors, and communication systems that underpin this transformation, this book offers a multidisciplinary view of the Tactile Internet revolution.This is an essential read for academic researchers, students, industry professionals, and R&D engineers seeking a comprehensive, up-to-date, and forward looking approach to understanding the Tactile Internet’s intersection with the Metaverse, together with its far reaching implications.

    • Industry 4.0 and Sustainability

      • 1st Edition
      • January 1, 2026
      • Muhammad Imran Khan + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 2 8 8 0 0
      • eBook
        9 7 8 0 4 4 3 3 2 8 8 1 7
      The Fourth Industrial Revolution, commonly termed Industry 4.0, is transforming production and manufacturing through integration of advanced digital technologies like artificial intelligence (AI), industrial internet of things (IIoT), big data analytics, additive manufacturing, and advanced robotics. While much discussion has centered on potential economic impacts, Industry 4.0 also holds significant implications for sustainability across environmental, social, and economic dimensions. However, simply adopting technologies does not guarantee sustainability benefits, and improperly managed, they pose risks related to job losses, inequality, and rebound effects that must be addressed through integrative frameworks. Industry 4.0 and Sustainability will provide the first comprehensive, evidence-based perspective unifying technological, organizational, policy, and social considerations to strategically direct Industry 4.0 innovations towards improved sustainability across economic, environmental, and social dimensions. With applied case studies and integrated roadmaps spanning engineering, management, and policy, it fills a crucial gap in guiding Industry 4.0 deployments to accelerate progress on sustainable development goals.

    • Medical Imaging Application in Health Assessment and Disease Management

      • 1st Edition
      • January 1, 2026
      • Dilber Uzun Ozsahin + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 8 9 6 7 5
      • eBook
        9 7 8 0 4 4 3 2 8 9 6 8 2
      Medical Imaging Application in Health Assessment and Disease Management is an all-encompassing book that explores the transformative power of medical imaging in various fields of medicine. It showcases the latest advancements and applications of medical imaging modalities, ranging from neurology and oncology to audiology and osteoporosis. The book highlights the role of medical imaging in understanding and treating neurological conditions, assessing bone health, unraveling hearing disorders, and diagnosing and treating oncological conditions. It also delves into the potential of artificial intelligence and machine learning in improving cancer diagnosis and treatment. The book explores the use of medical imaging in observing mental health conditions such as autism spectrum disorder and stress-related behavioral changes. This comprehensive resource is essential for researchers and professional engineers in the fields of medical image computing/processing... computer science, artificial intelligence, radiology, neuroscience, and biomedical research.

    • Edge Intelligence

      • 1st Edition
      • January 1, 2026
      • Jawad Ahmad + 5 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 8 2 9 7 0
      • eBook
        9 7 8 0 4 4 3 3 8 2 9 8 7
      Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.

    • Fundamentals of Statistics for Researchers and Data Analysts

      • 1st Edition
      • January 1, 2026
      • Shashi Chiplonkar + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 7 5 1 8
      • eBook
        9 7 8 0 4 4 3 4 4 7 5 2 5
      Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

    • Challenges and Applications of Generative Large Language Models

      • 1st Edition
      • January 1, 2026
      • Anitha S. Pillai + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 5 9 2 1
      • eBook
        9 7 8 0 4 4 3 3 3 5 9 3 8
      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.

    • Artificial Intelligence in Brain Disorders

      • 1st Edition
      • April 1, 2026
      • Pranav Kumar Prabhakar + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 7 2 2 1
      • eBook
        9 7 8 0 4 4 3 2 7 7 2 3 8
      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. This book offers a detailed overview of AI and machine learning techniques relevant to neurological research. The book 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.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

Information systems

Internet and web technology

Software

Theory and mathematics