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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.

  • Symbiotic Planning for Urban Futures

    Empowering Collaborative Human-AI Planning
    • 1st Edition
    • August 1, 2026
    • Zhong-Ren Peng
    • English
    AI-Driven Urban Planning: Shaping the Future of Cities presents a comprehensive guide to the transformative potential of artificial intelligence in urban planning. This book equips readers with the knowledge to harness data, analytics, and AI for creating sustainable, equitable, and livable urban environments. Exploring diverse applications—from understanding human mobility patterns to enhancing disaster response strategies and optimizing design processes—the book offers practical projects and illustrates how AI is shaping contemporary urban landscapes. By addressing both theoretical and practical dimensions, this resource aims to empower students, professionals, and policymakers with a holistic understanding of Urban Planning AI.It is organized into five parts, each tackling crucial aspects of Urban Planning AI. It first introduces core concepts, types, mechanisms, and ethical considerations surrounding AI. Part II then discusses the history of computer applications in urban and regional planning. Part III focuses on AI Applications in Urban Planning, addressing critical domains such as transportation, environmental, housing, economic, participatory, and health and safety planning. Part IV tackles challenges and ethical considerations, emphasizing equity, transparency, and data-related issues. Lastly, Part V explores future pathways of urban planning AI, discussing current trends, future visions, and interdisciplinary approaches essential for effective governance and policymaking.

  • Clinical Cancer Genomic Medicine

    • 1st Edition
    • August 1, 2026
    • Eamonn Maher + 3 more
    • English
    Clinical Cancer Genomic Medicine covers all major aspects of genetics and genomics in cancer development, diagnosis, and management, and their use in the clinical setting. The book explores not only the genetic basis of cancer, but also the role genomics plays in understanding the molecular basis of disease and how it can aid in diagnosis and treatment. Sections discuss the basics of oncogenomic and emerging technologies and specific cancer types, such as breast, gastrointestinal, ocular, skin, and pediatric. The book also includes knowledge on diagnostic cancer genomic techniques, surgical approaches, and ethical, legal, and social implications.This is a valuable resource for oncologists, clinicians, researchers, healthcare workers, and members of the biomedical field who need to understand more about the benefits of genetics and genomics for cancer and their applications in the clinical setting.

  • LLMs in Practice

    Real World Applications, Challenges & Success Stories
    • 1st Edition
    • August 1, 2026
    • Kiran Jot Singh + 3 more
    • English
    LLMs in Practice: Real World Applications, Challenges and Success Stories offers a deeply applied, interdisciplinary perspective on how Large Language Models (LLMs) are being integrated into the real world—spanning industries, healthcare, education, governance, mental health, creative domains, and intelligent systems. The book presents a blend of technical insights, sector-specific applications, governance frameworks, and ethical considerations. Designed for both academic and professional audiences, it equips readers to responsibly deploy LLMs while fostering innovation, equity, and scalability. LLMs in Practice: Real World Applications, Challenges & Success Stories addresses a significant gap in current literature by offering a focused and practice-oriented examination of how Large Language Models (LLMs) are being applied across diverse real-world domains. While there is widespread academic and public interest in generative AI, there exists no single resource that cohesively captures its deployment frameworks, sector-specific applications, ethical considerations, and pedagogical integration—especial... from a multidisciplinary and global perspective. This book provides deployment guidance, prompt optimization, and reliability strategies; governance frameworks, risk mitigation tools, and audit strategies; and offers case studies, instructional models, project templates, career-aligned examples, and skill-building paths.

  • A Comprehensive Guide to R Programming for Data Analytics

    • 1st Edition
    • August 1, 2026
    • Parul Acharya
    • English
    A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are thoroughly explained through real-world and publicly available data sets. R codes are presented in a manner that helps readers understand the program code syntax. Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world data sets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

  • Advanced Computational and Mathematical Approaches in Applied Differential Equations

    • 1st Edition
    • August 1, 2026
    • Snehashish Chakraverty + 2 more
    • English
    Advanced Computational and Mathematical Approaches in Applied Differential Equations explores cutting-edge techniques and methodologies in solving complex differential equations, a cornerstone of mathematical modelling across science and engineering. This book bridges theory and application, offering advanced computational strategies and innovative mathematical insights to address real-world problems. Ideal for researchers, practitioners, and advanced students, it provides a comprehensive resource for tackling challenging applied differential equations with better precision and efficiency. Differential equations are fundamental to modelling complex systems, yet solving them often involves significant challenges due to their complexity and nonlinearity. This book equips readers with advanced tools and methodologies to overcome these challenges, providing innovative solutions that improve accuracy, efficiency, and applicability in real-world scenarios. Beginning with an overview that presents a unified framework defining the types of differential equations covered (e.g. ordinary, partial, fractional, fuzzy), then progressing to foundations and methods such as Lie symmetries, homotropy, Adomian, FEM, FDM, spectral, machine learning, fuzzy, and fractional derivatives, the book addresses both computational and mathematical dimensions, empowering readers to solve applied differential equations more effectively.

  • AI-Powered Developments in Medical Robotics

    Data-Driven Techniques for Enhanced Surgical Efficiency
    • 1st Edition
    • August 1, 2026
    • Thomas Heinrich Musiolik + 3 more
    • English
    AI-Powered Developments in Medical Robotics: Data-Driven Techniques for Enhanced Surgical Efficiency offers a comprehensive exploration of AI-driven innovations, robotics, and data-driven techniques specifically tailored for medical applications. This book strikes a balance by addressing foundational principles, emerging technologies, and their practical implementation in real-world scenarios. It enhances its value through the inclusion of real-world case studies and interdisciplinary perspectives, making it relevant for professionals, researchers, and students alike. The book explores future developments, such as augmented and virtual reality in medical robotics, positioning itself as a forward-thinking resource. By addressing current gaps in the field, including regulatory challenges, training needs, and cost-effectiveness, it ensures a well-rounded approach that appeals to both advanced and emerging markets. This multifaceted perspective enriches the reader's understanding and equips them with actionable insights for navigating the complexities of AI-driven healthcare robotics. The book serves as a definitive reference for a global audience seeking innovation and practical solutions in the rapidly evolving landscape of medical technology, bridging the gap between theory and practice in a critical area of healthcare advancement.

  • Digital Twin Technology and Smart Grid

    • 1st Edition
    • August 1, 2026
    • Iraklis Varlamis + 2 more
    • English
    The integration of Digital Twin (DT) technology with Smart Grids (SG) represents a groundbreaking development in energy management, making this a highly significant and timely topic. As urban areas expand and energy demands rise, the need for more efficient, sustainable, and resilient energy systems becomes critical. DT technology, with its ability to create real-time, virtual replicas of physical systems, offers unprecedented opportunities for enhancing the performance, reliability, and security of smart grids. As the global energy landscape moves toward greater sustainability and decentralization, digital twins present unprecedented opportunities to enhance grid efficiency, bolster resilience, and support the integration of renewable energy sources. Digital Twin Technology and Smart Grid explores the intersection of these two technologies, which are essential for the evolution of energy systems. It explains how it utilises intelligent wireless sensor networks, the internet of things, artificial intelligence, machine learning, cloud, edge and fog-computing to monitor power consumption, to bring efficiencies and sustainability to our industry and human lives. It discusses how the the security risks and privacy challenges can be accommodated, and explains the ethical/legal implications of collecting data on a massive scale.

  • Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing

    • 1st Edition
    • August 1, 2026
    • Parikshit Narendra Mahalle + 1 more
    • English
    Digital twin computing is the bridge between the real and virtual worlds. Digital twin computing also is the mirror that reflects the real world into the virtual world. Blockchain technology can refine the digital twins (DTs) by ensuring transparency, decentralized data storage, data immutability, and peer-to-peer communication in various applications. DT provides a powerful tool able to generate a huge amount of training data for machine learning algorithms (MLAs). On the other hand, AI/ML based/driven DT offers many advantages for optimization, prediction, damage detection/predictive maintenance/predicti... modeling/decision support, lifecycle management for the real physical assets. Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing offers a comprehensive exploration of the synergy between artificial intelligence, machine learning, blockchain technology, and digital twin computing. Structured into three main sections, the book begins with a foundational overview of each technology, establishing a clear understanding of their individual roles and potential when combined. The second section delves into the integration of these technologies, focusing on key themes such as enhancing system simulations, ensuring data integrity, and enabling secure, real-time decision-making. Practical applications and case studies are used to illustrate how this convergence can drive innovation in industries like manufacturing, healthcare, and smart cities. The final section looks ahead, discussing emerging trends, challenges, and future opportunities in this evolving field. By blending theory with practical insights, this book serves as both an educational resource and a practical guide for professionals, researchers, and students seeking to harness the power of these advanced technologies in complex, real-world environments.

  • Encyclopedia of Multi-Attribute Decision Making (MADM)

    • 1st Edition
    • August 1, 2026
    • Gholamreza Haseli + 2 more
    • English
    Encyclopedia of Multi-Attribute Decision Making (MADM) is a comprehensive guide that presents all of the current methods in MADM in one volume. In recent years, numerous MADM methods have been introduced, each of them having been rapidly developed by numerous researchers using fuzzy sets and fuzzy numbers. This book presents all the existing methods in a simple way, including Sections on Weighting Methods, Extensions for the MADM Methods, Ranking Methods, and Outranking Methods. All methods chapters have a consistent structure, enabling easier learning of the methods. Each of the methods chapters presents two numerical examples for each method, one simple example with less than six criteria and six alternatives, and one complex example with six or more criteria and six or more alternatives. In addition, most of the methods chapters are written by the original developers of the method, ensuring insight into and practical application of MADM. The book is also filled with over 200 full-color figures illustrating the methods and their applications. Encyclopedia of Multi-Attribute Decision Making (MADM) demystifies the complex world of MADM, blending theoretical concepts with hands-on practices and case studies. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of MADM.

  • Federated Intelligence in Medicine

    AI-Driven Robotics for Secure and Intelligent Healthcare Systems
    • 1st Edition
    • August 1, 2026
    • Manisha Guduri + 3 more
    • English
    Federated Intelligence in Medicine: AI-Driven Robotics for Secure and Intelligent Healthcare Systems explores the rapidly evolving field where medical robotics intersects with cutting-edge artificial intelligence and secure data-sharing technologies. As healthcare demands more precise, less invasive, and personalized treatment options, the integration of Generative AI and Federated Learning has become essential. This book addresses the critical need to understand how these technologies collectively transform medical robotics, enabling adaptive, secure, and efficient healthcare solutions that leverage decentralized patient data while safeguarding privacy. Covering a comprehensive range of topics, the book begins with foundational knowledge in medical robotics and Generative AI, progressing to advanced concepts such as federated learning and architectural modeling tailored for AI-driven robotic systems. Readers will explore algorithms enhancing surgical robotics, remote robotic surgeries, and human-robot interaction. The text also tackles AI-driven diagnostic robots, privacy preservation through homomorphic encryption and differential privacy, and emerging medical imaging techniques integrated with robotics. Later chapters examine the synergy of large language models, agent AI, and edge AI with robotic technologies, providing both theoretical frameworks and practical case studies that illuminate real-world applications and challenges. This book is an invaluable resource for researchers and academicians engaged in artificial intelligence, robotics, and medical technology. It also serves undergraduate and graduate students in Biomedical Engineering, Electronics and Electrical Engineering, and Computer Science and Engineering. By bridging the gap between AI-driven decision-making, robotics implementation, and secure data-sharing, this book equips readers with the knowledge to pioneer advancements that will define the future of intelligent and secure healthcare systems.

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