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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.

  • Agile Systems Engineering with SysML v2 and AI

    • 2nd Edition
    • Bruce Powel Douglass
    • English
    Agile Systems Engineering with SysML v2 and AI, Second Edition presents a practical vision of systems engineering in which requirements, structure, behavior, and analysis are captured as precise engineering data—while still addressing the “big system” concerns of safety, security, reliability, privacy, and performance in an agile context. World-renowned author and speaker Dr. Bruce Powel Douglass shows how agile methods, model-based systems engineering (MBSE), and artificial intelligence (AI), work together to reduce ambiguity, expose defects earlier, and sustain end-to-end traceability from stakeholder intent to verification evidence.This edition goes beyond concepts by providing usable, repeatable workflows for modern programs—covering incremental, agile, and DevSecOps-oriented lifecycles and the concrete process steps and gates that make them executable in practice. Rather than treating modeling as documentation, the book treats SysML v2 as a semantic backbone for capturing requirements, architecture, interfaces, behaviors, constraints, and verification intent in one coherent source of truth.New to this edition is an introduction to SysML v2 and an entire chapter on AI and modern MBSE, showing where AI assistants provide leverage, how to apply quality-control gates to keep outputs trustworthy, and how to integrate AI into real engineering workflows without surrendering correctness. Each chapter includes AI prompt patterns for MBSE—ready-to-use prompt structures for generating SysML v2 model elements, extracting and normalizing requirements from external sources, reconciling terminology, and reviewing models against project rules and acceptance criteria. Throughout, Douglass equips systems engineers with concrete methods to prevent specification defects, improve system quality, and reduce rework—so teams can move faster and build with greater confidence

  • 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.

  • AI-Driven Diagnostics for 6G-Enabled Smart Healthcare

    • 1st Edition
    • Sangeeta Kumari + 3 more
    • English
    AI-Driven Diagnostics for 6G-Enabled Smart Healthcare explores the transformative integration of artificial intelligence (AI) and next-generation 6G networks in the healthcare sector. The book begins by highlighting the evolution of healthcare technology and the critical role of AI-driven diagnostics, emphasizing how 6G facilitates real-time, ultra-reliable communication. Key features of 6G, such as ultra-low latency and massive connectivity, are discussed, showcasing their impact on advanced healthcare applications like remote diagnostics and patient monitoring. The integration of AI in medical diagnostics is examined, focusing on machine learning and deep learning techniques that enhance disease detection through medical imaging and clinical data analysis. The book explores the benefits of remote patient monitoring, particularly for underserved populations, and explores edge AI for localized, low-latency diagnostic processing. Real-time imaging diagnostics are highlighted, demonstrating how 6G supports rapid transfer and analysis of high-resolution medical images. It also addresses predictive analytics, detailing AI models that forecast diseases and the role of IoT devices and wearables in healthcare diagnostics. Concepts of smart hospitals and the integration of blockchain technology for security and data integrity are discussed. Ethical considerations and regulatory challenges are thoroughly examined, ensuring patient privacy and compliance. The book concludes with insights into future trends and emerging technologies in AI diagnostics, including quantum AI and next-generation sensors.

  • 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.

  • 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.

  • Neuro-Symbolic AI

    Integrating Neural Networks and Symbolic Reasoning
    • 1st Edition
    • Sarika Jain + 3 more
    • English
    Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning explores the convergence of two historically distinct paradigms in artificial intelligence—data-dr... neural networks and logic-based symbolic reasoning. This book presents a comprehensive roadmap of this emerging hybrid discipline, offering deep theoretical insights, practical methodologies, and transformative applications across diverse research sectors including healthcare, finance, engineering, and autonomous systems. While neural networks have achieved remarkable success in perception and pattern recognition tasks, they often lack the reasoning, transparency, and generalizability that symbolic systems excel at. Conversely, symbolic AI lacks the flexibility and scalability of deep learning. This handbook directly addresses these challenges by providing a structured approach to Neuro-symbolic AI, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques (e.g., knowledge graphs, compositionality, category theory), and diverse real-world applications. This book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume. The book is structured into four parts—Foundational Principles, Hybrid Models and Techniques, Real-World Applications, and Emerging Challenges. It brings together cutting-edge research and expert perspectives to highlight how Neuro-Symbolic AI enhances interpretability, reasoning capabilities, and trust in intelligent systems. This book addresses the critical challenge faced by AI researchers and practitioners: how to build intelligent systems that combine the learning capacity of neural networks with the reasoning ability of symbolic methods. Readers often struggle with the lack of unified frameworks, practical tools, and clear guidance for integrating these two approaches. This book provides readers with a comprehensive, structured, and interdisciplinary resource that captures the evolving landscape of Neuro-Symbolic AI.

  • 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.

  • 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.

  • 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.

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