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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

    • AI Platforms as Global Governance for the Health Ecosystem

      • 1st Edition
      • May 1, 2026
      • Dominique J. Monlezun
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 5 0 5 6
      • eBook
        9 7 8 0 4 4 3 4 5 5 0 6 3
      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 as global governance for the healthcare ecosystem. 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 book explores how AI platforms can transform hospitals and clinical practice by digitally unifying patients, providers, and payors, advancing healthcare for all. This book defines and explains the main hurdles and technical innovations in responsibly governing AI platforms for efficient, equitable, and sustainable global healthcare. It explores the history, science, politics, economics, ethics, policy, as well as the future of these AI platforms, and how governance efforts can work toward the common good.

    • Understanding Models Developed with AI

      • 1st Edition
      • May 1, 2026
      • Ömer Faruk ErtuÄŸrul + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 1 6 3 9
      • eBook
        9 7 8 0 4 4 3 4 4 1 6 4 6
      Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide for readers looking to understand the intricacies of AI models and their real-world applications. This book demystifies complex AI methodologies by providing clear explanations and practical examples, reinforced with Python and MATLAB program code. It is an essential resource for readers who aim to develop and interpret AI models effectively. The primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results and bias (data and algorithm) management. Researchers and developers need to be able to not only implement AI models, but also to interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable. This book is a valuable reference that equips readers with the tools 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. The book’s content structure emphasizes a practical, application-driven approach to understanding AI models, with hands-on coding examples throughout each chapter.

    • Smart Wearable IoT

      • 1st Edition
      • May 1, 2026
      • Shuenn-Yuh Lee + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 7 0 0 7
      • eBook
        9 7 8 0 4 4 3 3 6 7 0 1 4
      Smart Wearable IoT: Principles and Implementation of Development Modules with Wireless Biomedical SoC focuses on the development of intelligent wearable technology integrated with the Internet and various platforms. The book provides detailed guidance on building a user-friendly development platform that features intelligent wearable systems, including bio-signal SoCs/modules, user-friendly websites/apps, and artificial intelligence (AI) systems on edge/cloud. Through wireless bio-signal acquisition, readers can continuously access and monitor their vital signs via the wearable platform. By exploring specific case studies, such as the ECG-based fatigue analysis system, readers will gain fundamental knowledge in biosignal acquisition and processing. This hands-on approach enables them to understand the integration of digital signal processing and artificial intelligence in analyzing physiological data, ultimately enhancing their skills in developing innovative wearable solutions.

    • Engineering Generative AI-Based Software

      • 1st Edition
      • May 1, 2026
      • Miroslaw StaroÅ„
      • English
      • Paperback
        9 7 8 0 4 4 3 2 7 6 0 6 4
      • eBook
        9 7 8 0 4 4 3 2 7 6 0 7 1
      Software Engineering professionals now face challenges in incorporating GAI into the products and programs they are developing. At this point, the knowledge about developing AI-based software is mostly based on classical AI, i.e., non-generative ML systems. Developers know how to use machine learning and, to some extent, how to include it in production systems. Engineering Generative-AI Based Software takes software development to the next level by using generative AI instead. Readers learn how to use text, image and audio models as part of larger software systems. The book discusses both the process of developing such software and the architectures for this kind of software, combining theory with practice. Generative AI software is gaining popularity thanks to such models as GPT-4 or Llama. More and more products use them as part of their feature portfolio, but this software is often limited to web applications or recommendation systems. Author Miroslav Staron shows readers how to tackle the challenges of professionally engineering generative AI-based systems. The book starts by reviewing the most relevant models and technologies in this area, both theoretically and practically. Once readers know the technologies, the book goes into details of software engineering practices for such systems, e.g., eliciting functional and non-functional requirements specific to generative AI, various architectural styles and tactics for such systems, and different programming platforms. The book also shows how to create robust licensing models and the technology to support them. Finally, readers learn how to manage data, both during the training and also when generating new data, as well as how to use the generated data and user feedback to constantly evolve generative AI-based software.

    • Deep Learning Applications in Neuroinformatics

      • 1st Edition
      • May 1, 2026
      • Karthik Ramamurthy
      • English
      • Paperback
        9 7 8 0 4 4 3 4 1 4 5 9 6
      • eBook
        9 7 8 0 4 4 3 4 1 4 6 0 2
      Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics. This book covers the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. It addresses critical neurological disorders like Alzheimer’s disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder. The book bridges the gap between neuroscience and artificial intelligence, making it ideal for researchers, practitioners, and students. It offers insights from leading experts and provides a clear pathway from fundamental concepts to advanced research and future trends in the field.

    • Distributed AI in the Modern World

      • 1st Edition
      • May 1, 2026
      • Andrei Olaru + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 6 7 9 5
      • eBook
        9 7 8 0 4 4 3 4 4 6 8 0 1
      Distributed AI in the Modern World: Technical and Social Aspects of Interacting Intelligent Agents presents several state-of-the-art insights into the various forms of distribution of artificial intelligence, with practical application instances. 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 environment along with practical solutions at an architectural level. Deployment, maintenance and monitoring of distributed machine learning systems brings about many practical challenges, dealing with the intelligent agents distributed across a network of heterogenous devices, or interacting with robots and humans alike. While these scenarios are very different, some challenges remain the same when interaction exists: discoverability, availability, communication language and formats, and efficiency in transferring significant amounts of information. The book provides readers with practical solutions at an architectural level, with solutions presented in three parts. Part 1 deals with the distribution of the learning process and the utilization of machine learning models in a distributed system. Part 2 deals with tools that enable the distribution and interaction of artificial learning entities and how multi-agent systems and machine learning can be combined. Part 3 deals with the physical embodiment of intelligent agents and the interaction of intelligent computing units bound to physical space. The three parts are followed by a conclusion, emphasizing the challenges that are common to all scenarios and solutions which apply in a wider range of cases.

    • Data Compression for Data Mining Algorithms

      • 1st Edition
      • May 1, 2026
      • Xiaochun Wang
      • English
      • Paperback
        9 7 8 0 4 4 3 4 0 5 4 1 9
      • eBook
        9 7 8 0 4 4 3 4 0 5 4 2 6
      Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs. Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view. Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.

    • Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

      • 1st Edition
      • May 1, 2026
      • Manuel González Canché
      • English
      Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools—just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

    • Advances in Medical Imaging

      • 1st Edition
      • May 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.

    • Metaverse and AI in Healthcare

      • 1st Edition
      • May 1, 2026
      • Jyotir Moy Chatterjee + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 9 5 8 1
      • eBook
        9 7 8 0 4 4 3 4 4 9 5 9 8
      Metaverse and AI in Healthcare: A Federated Learning Approach addresses the transformative integration of artificial intelligence and metaverse technologies in healthcare. It fills a critical gap by exploring how federated learning enables secure, decentralized data sharing and personalized medicine in virtual health platforms, meeting urgent demands for privacy, interoperability, and innovation. The book is structured into four parts covering foundational AI and federated learning concepts, augmented reality and metaverse applications, legal and cybersecurity challenges, and emerging strategic trends. Contributors from academia and industry present chapters on predictive modeling, cybersecurity frameworks, AR fitness, legal perspectives, and AI-driven medical tourism, supported by case studies and technical explanations. This reference equips graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare with practical knowledge and forward-looking analysis. It empowers readers to navigate evolving digital health ecosystems, addressing data privacy, customized care, and global access challenges through federated learning and metaverse solutions.

Related subjects

Applications

Artificial 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