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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.

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

    • 1st book:metaData.edition
    • Parikshit Narendra Mahalle contributors.plusContributors
    • publicationLanguages:en
    Artificial Intelligence, Machine Learning and Blockchain in Digital Twin Computing explores the synergy between artificial intelligence, machine learning, blockchain technology, and digital twin computing. The book overviews 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. Final sections look ahead, discussing emerging trends, challenges, and future opportunities.Digita... 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).

  • LLMs in Practice

    Real World Applications, Challenges and Success Stories
    • 1st book:metaData.edition
    • Kiran Jot Singh contributors.plusContributors
    • publicationLanguages:en
    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.

  • Full-Stack Web Development from the Ground Up

    Principles, Practices, and Technologies
    • 1st book:metaData.edition
    • Christopher D Hundhausen
    • publicationLanguages:en
    Full-Stack Development from the Ground Up: Principles, Practices, and Technologies addresses the growing need for a comprehensive upper-division computer science textbook that provides in-depth treatment of full-stack web development using the modern web development technologies that students are likely to encounter in industry. Professional full-stack web developers who are capable of developing both the front-end user interfaces and back-end databases and services for dynamic websites are in high demand. The book begins by laying a foundation in HTML, CSS and JavaScript—the building blocks of client-side web development.It then explores one particular web development stack in detail: MERN, which stands for MongoDB, Express.js, React.js and Node.js. Together, these four technologies provide powerful support for full-stack web development in a single programming language—JavaScript. The crucial final step in the web development process is deploying apps to a server, so users can interact with them. This book simplifies deployment by focusing on just one web deployment environment: Amazon Web Services (AWS), and only those AWS tools that are absolutely necessary to deploy MERN applications.

  • Deep Learning in Bioinformatics

    Techniques and Applications in Practice
    • 2nd book:metaData.edition
    • Habib Izadkhah
    • publicationLanguages:en
    Deep Learning in Bioinformatics: Techniques and Applications in Practice, Second Edition explores how deep learning can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology. The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions. This updated edition includes several new chapters, applications, and examples for new Deep Learning advances and techniques.Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.

  • Essential Kubeflow

    Engineering ML Workflows on Kubernetes
    • 1st book:metaData.edition
    • Prashanth Josyula contributors.plusContributors
    • publicationLanguages:en
    Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. 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 solutions for real-world challenges.With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.

  • GeoAI for Earth Observation Imagery

    Fundamentals and Practical Applications
    • 1st book:metaData.edition
    • Dalton Lunga contributors.plusContributors
    • publicationLanguages:en
    GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications comprehensively covers methodologies of AI and Machine Learning applications of image processing for Earth Observation (EO) Imagery. As traditional image processing methods face challenges with handling vast volumes of EO imagery, leading to efficiencies and limitations when extracting meaningful insights, AI-driven approaches can enhance the efficiency, accuracy, and scalability of image processing. Chapters cover essential methodologies including atmospheric compensation, image enhancement techniques like deblurring and superresolution, and advanced analysis methods such as semantic segmentation and object detection.Cutting-ed... approaches to computing, automating, and optimizing image processing tasks are also covered. Additionally, emerging trends in GeoAi and their implication on future research are reviewed. The book serves as an essential guide for navigating the complexities of spatial data and equips readers with knowledge to enhance their analytical capabilities.

  • Up and Running with AutoCAD 2027

    2D and 3D Drawing, Design and Modeling
    • 1st book:metaData.edition
    • Robert C. Kaebisch
    • publicationLanguages:en
    Up and Running with AutoCAD® 2027: 2D and 3D Drawing, Design and Modeling presents a combination of step-by-step instructions, examples, and insightful explanations. The book emphasizes core concepts and practical application of AutoCAD in engineering, architecture, and design. Equally useful in instructor-led classroom training, self-study, or as a professional reference, the book is written by a long-time AutoCAD professor and instructor with the user in mind. To improve student learning, the early chapters have been revised and expanded with additional assignments, more explicit step-by-step guidance for the earliest assignments, improved readability, and a new concise drafting fundamentals reference section focusing on orthographic projection, basic standards, and naming conventions.

  • Autonomous Vehicle Safety Solutions

    Foundations, Technologies, and Prospects for the Future
    • 1st book:metaData.edition
    • Aparna Kumari
    • publicationLanguages:en
    Autonomous Vehicle Safety Solutions: Foundations, Technologies, and Prospects for the Future addresses the dynamically evolving state of the art that traverses and brings together automotive engineering, electronics engineering, and computer science. Its technical overview of smart vehicles' capabilities, propelled by headway in artificial intelligence and sensor technologies, enables in-depth understanding of the safety-assurance complexities that guide their design and operation.The comprehensive volume distinguishes itself for its treatment of several exciting, emerging opportunities and trends, including machine learning algorithms, V2X connectivity, and cybersecurity. Up-to-date applications are featured through practical examples and expert insights, bringing additional value to a discourse which underscores the critical need for ongoing safety advancements and adaptability in the fast-moving context of autonomous driving systems, with their related implications on sustainability and society as a whole.The outcome is a cornerstone resource for academia and industry alike that encourages further transformative, interdisciplinary investigations to bring safety solutions to maturity and subsequent rigorous testing for validation standards to be defined, thus realizing, in a future not distant from now, a scenario where self-driving vehicles can co-exist seamlessly with traditional modes of transportation and also integrate reliably, efficiently, and without risks within the larger infrastructure.

  • Advanced Intelligence Methods for Data Science and Optimization

    • 1st book:metaData.edition
    • Amir Hossein Gandomi contributors.plusContributors
    • publicationLanguages:en
    Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

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

    • 1st book:metaData.edition
    • Manuel González Canché
    • publicationLanguages:en
    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.

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