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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

    • 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.

    • Digital Twins

      • 1st Edition
      • May 1, 2026
      • Bedir Tekinerdogan + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 5 7 3 5
      • eBook
        9 7 8 0 4 4 3 4 5 5 7 2 8
      Digital Twins: Core Principles and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. As digital twin technology evolves, its integration with various advanced digital solutions is becoming essential for achieving real-time insights and autonomous decision-making. Challenges include understanding the interoperability of these technologies, managing data complexity, ensuring security, and optimizing for low-latency environments. The authors demystify digital twin technology, providing a clear framework for understanding how to effectively implement and utilize digital twins. The book addresses common challenges such as data integration, security, scalability, and the alignment of digital twin models with actual physical processes. After presenting core concepts of digital twins for software engineering, the book progresses to a section on integration with advanced digital solutions such as AI, IoT, Cloud computing, Big Data Analytics, and Extended Reality (XR). Next, the authors provide readers with a thorough presentation of digital twins applications in a variety of settings and industry/research topics. Finally, the book concludes with a discussion of challenges and solutions, along with future trends in digital twins research and development.

    • Artificial Intelligence and Machine Learning for Safety-Critical Systems

      • 1st Edition
      • May 1, 2026
      • Rajiv Pandey + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 5 9 7 3
      • eBook
        9 7 8 0 4 4 3 3 6 5 9 8 0
      Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide serves as a vital reference for engineers and system designers seeking to integrate AI and ML techniques into safety-critical environments. The book is meticulously structured into nine sections, each focusing on core applications and challenges unique to these high-stakes systems. Readers are guided through strategies that optimize resources, minimize failures, and bolster both system and public safety. With its practical approach, the guide aims to bridge the gap between advanced AI solutions and the rigorous demands of safety-critical industries.The book also delves into diverse domains such as pattern recognition, image processing, edge computing, IoT, encryption, and hardware accelerators. Each application area is explored to reveal the unique hurdles and solutions in deploying ML models in safety-sensitive contexts. Finally, the authors also emphasize the importance of explainable AI, ensuring model outputs are transparent and trustworthy rather than opaque. To further strengthen confidence in these systems, the text discusses legal, certification, and regulatory aspects, equipping readers with the tools necessary to achieve compliance and public trust.

    • 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.

    • 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.

    • 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.

    • Essential Kubeflow

      • 1st Edition
      • May 1, 2026
      • Prashanth Josyula + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 5 2 5 4 3
      • eBook
        9 7 8 0 4 4 3 4 5 2 5 5 0
      Essential Kubeflow: Engineering ML Workflows on Kubernetes equips readers with the tools to transform ML workflows from experimental notebooks to production-ready platforms with this comprehensive guide to Kubeflow, one of the most widely adopted open source MLOps platforms used to automate ML workloads. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides practical solutions for real-world challenges. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms: architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, scaling workloads efficiently, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. By the end of this book, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

    • AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

      • 1st Edition
      • May 1, 2026
      • Olfa Boubaker + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 5 5 4 6
      • eBook
        9 7 8 0 4 4 3 3 6 5 5 5 3
      AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice explores the transformative role of AI and data science in enhancing precision medicine, predictive analytics, and medical practice. The book covers diverse topics such as AI-driven personalized medicine, seizure prediction through EEG analysis, and the application of chaos theory in AI-driven healthcare. The volume also delves into medical practice and education, including ethical considerations, AI-driven supply chain management, and clinical documentation using natural language processing.Furthermo... it examines AI's role in telemedicine, patient engagement, and adherence, offering innovative solutions to improve healthcare delivery and outcomes.

    • 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.

    • AI-Driven Human-Machine Interaction for Biomedical Engineering

      • 1st Edition
      • May 1, 2026
      • Kapil Gupta + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 4 6 3 8 2
      • eBook
        9 7 8 0 4 4 3 4 4 6 3 9 9
      AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies offers a comprehensive examination of the intricate relationship between humans and machines, particularly through the transformative lens of artificial intelligence (AI). As AI technologies rapidly evolve, understanding their implications for human-machine interaction (HMI) has become essential across various domains, especially healthcare. This book addresses the pressing need for insights into AI-driven methodologies, providing scholars, practitioners, and learners with foundational knowledge and practical applications that enhance collaboration between human cognition and machine capabilities. Structured into well-defined chapters, the book begins with an introduction to AI-driven HMI, laying the groundwork for understanding its significance in sustainable healthcare and beyond. Subsequent chapters explore critical topics such as machine learning principles, advanced biomedical data classification methods, and the role of AI in telemedicine. Readers will delve into cutting-edge techniques, from deep learning to non-invasive computer vision, and examine the implications of these technologies across industries. Each chapter equips readers with actionable insights and highlights emerging trends, ethical considerations, and the future of AI in HMI, ensuring a well-rounded perspective on this dynamic field. AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies is an invaluable resource for researchers, academics, and students in the fields of Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, and Healthcare Technology. By bridging theoretical foundations with practical applications, this book empowers its readers to effectively harness AI technologies, driving innovation and improving outcomes in healthcare and various sectors

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