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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

    • Digital Transformation and Equitable Global Health

      • 1st Edition
      • May 15, 2026
      • Arletty Pinel + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 4 9 8 1
      • eBook
        9 7 8 0 4 4 3 2 1 4 8 8 2
      Digital Transformation and Equitable Global Health: A Future-Ready Perspective presents a collective body of knowledge and global experiences that demonstrate current status and future trends in the use of exponential technologies and their potential for poverty reduction, improving health outcomes, strengthening health systems, and transforming traditional development aid structures. The book uses a translational innovation perspective to guide the reader—regardless of their area of expertise—on the rationale behind the co-creation of human-centered, affordable, and sustainable digital solutions.It addresses the interest of professionals from multiple areas (e.g., technology, health, social development, global financing), and it is a valuable resource for professionals, social scientists, practitioners, researchers, instructors, and undergraduate and graduate students interested in understanding the challenges and complexities of global public health and the applied uses of health technologies for equitable access to primary health care and universal health coverage.

    • Synthetic Media, Deepfakes, and Cyber Deception

      • 1st Edition
      • May 1, 2026
      • Cameron H. Malin + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 3 8 8 7 1
      • eBook
        9 7 8 0 4 4 3 2 3 8 8 8 8
      Synthetic Media, Deepfakes, and Cyber Deception: Attacks, Analysis, and Defenses introduces the only analytical Synthetic Media Analysis Framework (SMAF) to help describe cyber threats and help security professionals anticipate and analyze attacks. This framework encompasses seven dimensions: Credibility, Control, Medium, Interactivity, Familiarity, Intended Target, and Evocation. Synthetic media is a broad term that encompasses the artificial manipulation, modification, and production of information, covering a spectrum from audio-video deepfakes to text-based chatbots. Synthetic media provides cyber attackers and scammers with a game-changing advantage over traditional ROSE attacks because they have the potential to convincingly impersonate close associates through text, imagery, voice, and video.This burgeoning threat has yet to be meaningfully addressed through any written treatment on the topic. The book is co-authored by three cyber influence and deception experts who have gained deep knowledge and experience on the topic through diverse, true operational pathways and backgrounds. The diversity and perspectives of the author team makes the content in the book the broadest and deepest treatment of synthetic media attacks available to readers.

    • 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.

    • Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

      • 1st Edition
      • May 1, 2026
      • Hanif Heidari + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 4 1 3 3 5
      • eBook
        9 7 8 0 4 4 3 3 4 1 3 4 2
      Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders discusses the latest developed methods in IoT and its applications in neurological disorders that emphasize end-user requirements. The book explores Intelligent IoT and its use in exploring the intersection between medicine, data science, biomedical engineering, and healthcare systems. In addition, this release includes a comprehensive overview of modeling and analyzing the requirements of people with neurological disorders. Signals and images of biological activity are collected and analyzed based on patient specifications to facilitate more accurate diagnosis and treatment.Finally, the book also discusses cutting-edge AI methods for IoT devices designed to treat neurological conditions.

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

      • 1st Edition
      • May 1, 2026
      • Manuel González Canché
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 9 6 1 0
      • eBook
        9 7 8 0 4 4 3 2 1 9 6 0 3
      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.

    • 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.

    • 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.

    • Foundations of Human-Computer Interaction

      • 1st Edition
      • May 1, 2026
      • Robert Atkinson
      • English
      • Paperback
        9 7 8 0 4 4 3 4 1 3 5 9 9
      • eBook
        9 7 8 0 4 4 3 4 1 3 6 0 5
      Foundations of Human-Computer Interaction: Innovations and Future Trends covers the fundamentals of human-computer interaction (HCI), usability, and user-centred design. It provides a holistic and engaging exploration of HCI by integrating historical perspectives, behavioural and cognitive insights, neuroscientific principles, and advanced technological tools. This comprehensive approach ensures graduate students and undergraduate not only understand the theoretical frameworks but also see their practical applications in real-world scenarios. The pedagogy emphasizes interactive learning, critical thinking, and iterative design processes, making the content accessible and engaging for both novices and advanced learners. The book also discusses contemporary challenges such as dark patterns and surveillance capitalism, and offers an understanding of the ethics and code of conduct to enable the student and practitioner take their thinking and designs forward into the market place in a responsible way.

    • Pioneering Autonomous Technology: A Deep dive into Hyper Automation

      • 1st Edition
      • Volume 143
      • May 1, 2026
      • English
      • Hardback
        9 7 8 0 4 4 3 3 1 7 1 0 1
      • eBook
        9 7 8 0 4 4 3 3 1 7 1 1 8
      Pioneering Autonomous Technology: A Deep dive into Hyper Automation, Volume 143 in the Advances in Computers series, showcases the transformative role of artificial intelligence, deep learning, and machine learning in creating safer, more efficient, and socially inclusive environments. This volume explores how autonomous technologies are reshaping healthcare, transportation, agriculture, and urban living. Chapters cover cutting-edge applications such as deep learning–based medical diagnostics for chronic diseases, retinal biomarkers for early detection of Alzheimer’s and Parkinson’s, and superior CNN approaches for agricultural automation. The book highlights advances in autonomous vehicles, from industry trends and electric vehicle integration to safety and societal impacts, as well as essential topics like cybersecurity challenges and machine learning–driven life expectancy prediction. Broader societal applications include income inequality modeling, sentiment analysis in Indian languages, and IoT-based smart city initiatives, offering readers a comprehensive perspective on the future of intelligent and autonomous systems.

    • 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.

Related subjects

Applications

Artificial intelligence

Business 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