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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.

  • Development of Multi-Agent System Infrastructures

    A Practical Approach
    • 1st Edition
    • Andrei Olaru
    • English
    Development of Multi-Agent Systems Infrastructure: A Practical Approach explores the creation of modular frameworks to support the deployment of real-world software applications utilizing multi-agent systems (MAS). Drawing from the author’s hands-on experience with the FLASH-MAS framework—a Fast Lightweight Agent Shell—the book delves into both theoretical models and practical solutions for MAS implementation. It addresses the complexities of deploying autonomous agents across diverse fields such as manufacturing, robotics, health care, and supply chain management, highlighting the shared challenges developers face when managing distributed, networked, or large-scale agent interactions. The book is organized into three main sections, covering models and languages for MAS, the deployment and interaction between system entities, and practical guidance for implementing robust MAS frameworks. Emphasizing modularity, the author presents adaptable tools and solutions that can be independently utilized for system development and maintenance. Practical issues such as entity lifecycle management, environmental interactions, and system robustness are thoroughly examined, making this resource valuable for both new and experienced MAS developers.

  • Generative Artificial Intelligence for Neuroimaging

    Methods and Applications
    • 1st Edition
    • Deepika Koundal + 1 more
    • English
    Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. This book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare. It addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.

  • Foundations of Digital Twins

    • 1st Edition
    • Tuan Anh Nguyen + 2 more
    • English
    Digital twin computing is the bridge between the real and virtual worlds, and operates as a mirror that reflects the real world into the virtual world. Digital twin technologies take data from sensor networks in the real world, utilise artificial intelligence and machine learning to analyse the data, and then create a digital representation or simulation that updates as their real world counterpart updates. As such, the model can be used to understand the real world environment, and the digital twin can be used to make informed decisions that will be of benefit in the real world. Foundations of Digital Twins explains the fundamentals of digital twins, how digital twin technologies can gather real world data using the sensor networks that comprise the Internet of Things, and how that data can be sorted, analysed, and utilised to improve services and to increase sustainability. Structured in five sections, the book begins with an introductory overview of each technology, establishing a clear understanding of their individual roles and their potential when combined. The second section delves into data acquisition, featuring advanced sensors, drones, robots and actuators. Part Three considers data exchange and security, focusing on CPS, the internet of things, and blockchain. Part Four looks at how digital twins can impact on data computing, such as video streaming, cloud computing, fog computing and edge-computing. The final section explores the future opportunities and risks of adopting advanced technologies in this evolving field, including artificial intelligence, the ethical issues concerned when collecting and using data, and the security implications. By blending theory with practical insights, Foundations of Digital Twins serves as both an educational resource and a practical guide for researchers, students and professionals seeking to harness the power of these advanced technologies in complex, real world environments.

  • AI-Driven Optimization and Automation of Integrated Circuit Design

    • 1st Edition
    • Neha Singh + 2 more
    • English
    Artificial Intelligence (AI) offers a solution to the bottleneck issues in the design of integrated circuits (IC) by optimizing and automating tasks in the design and fabrication process. As the world focuses on the development of skilled manpower and automation tools for chip design, verification, testing and fabrication, AI can be utilised to optimize and automate various steps in design cycle, saving time, reducing errors, and managing power consumption. AI-Driven Optimization and Automation of Integrated Circuit Design discusses the latest AI-based methods, algorithms, architectures, and frameworks for digital, analog, and mixed-signal VLSI circuit design, verification and testability, physical design and related areas. It considers the issues with traditional circuit design, and explains how machine learning techniques can optimise and automate the process. It goes on to explain how AI-driven design can impact logic synthesis, circuit placement and routing, and behavioural simulation. It closes with cases studies and a look at future developments for implementations of VLSI design, IC design, and hardware realization using AI tools and techniques.

  • Intelligent Semantic Analysis for Healthcare

    • 1st Edition
    • Sonali Vyas + 3 more
    • English
    Intelligent Semantic Analysis for Healthcare explores the latest trends, developments, and future directions of intelligent semantic analysis techniques on retrieving and managing meaningful medical information for healthcare information systems. The book explores different computational methods, ideas, strategies, and techniques to analyze relevant healthcare information in an innovative and efficient way, thus bridging the gap between gathering and comprehending data with healthcare and biological applications. It offers a comprehensive view of intelligent semantic analysis in healthcare, bridging the gap between data collection and healthcare applications, and providing innovative computational methods for data analysis.Sections focus on intelligent semantic analysis rather than broader topics of big data and healthcare analytics. Additionally, the book is geared towards practical approaches and innovative techniques for state-of-the-art and current challenges in healthcare data management.

  • System of Systems Engineering

    Innovations, Challenges, and Future Directions
    • 1st Edition
    • Bedir Tekinerdogan + 1 more
    • English
    System of Systems Engineering: Innovations, Challenges, and Future Directions is an essential resource in the interdisciplinary field that addresses the design, management, and integration of complex systems operating independently yet interacting to achieve higher-level goals. With the increasing complexity of modern engineering challenges such as smart cities, defense systems, and global communication networks, there is a critical need for a deeper understanding of SoSE principles. Traditional systems engineering methods often fall short in addressing the dynamic, distributed, and interdependent nature of such systems. This book aims to fill that gap by providing a comprehensive guide that combines theoretical foundations with practical applications, making it crucial for both academic researchers and industry practitioners. The book is structured into five parts, each focusing on different aspects of System of Systems Engineering. Part I, Foundations of System of Systems Engineering, introduces the field, characterizes and classifies SoS, and discusses key concepts. Part II, Governance and Management of SoSE, covers strategic governance, policy and regulatory frameworks, and leadership and decision-making in SoSE projects. Part III, Methodologies and Tools, explores systems thinking and modeling approaches, lifecycle management, and interoperability and integration strategies. Part IV, AI and System of Systems Engineering, delves into leveraging AI for enhanced decision-making, machine learning applications, AI-driven automation and control, and ethical considerations. Finally, Part V, Case Studies and Emerging Challenges, presents real-world applications in defense and aerospace, smart cities, healthcare, environmental and energy systems, and discusses future directions and research opportunities. System of Systems Engineering: Innovations, Challenges, and Future Directions offers significant benefits to graduate students, researchers, and professionals in software engineering, systems engineering, aerospace engineering, defense, telecommunications, and other fields where SoSE is relevant. It is particularly useful for those involved in the design, management, and analysis of large-scale, complex systems. The content is also suitable for advanced undergraduate and postgraduate courses, as well as professional development programs focusing on SoSE, providing a thorough understanding and practical insights into this evolving field.

  • 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.

  • 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. The 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 policymakers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The book's 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.

  • 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.

  • 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.

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