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Elsevier

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.

  • LLMs in Practice

    Real World Applications, Challenges and Success Stories
    • 1st Edition
    • 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.

  • Deep Learning in Bioinformatics

    Techniques and Applications in Practice
    • 2nd Edition
    • Habib Izadkhah
    • English
    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.

  • Autonomous Vehicle Safety Solutions

    Foundations, Technologies, and Prospects for the Future
    • 1st Edition
    • Aparna Kumari
    • English
    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.

  • Essential Kubeflow

    Engineering ML Workflows on Kubernetes
    • 1st Edition
    • Prashanth Josyula + 2 more
    • English
    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.

  • Up and Running with AutoCAD 2027

    2D and 3D Drawing, Design and Modeling
    • 1st Edition
    • Robert C. Kaebisch + 1 more
    • English
    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. To improve student learning, the book's 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 that focuses on orthographic projection, basic standards, and naming conventions.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

  • Data Science, Interactive Visualizations, and Generative AI Tools for the Analysis of Qualitative, Mixed-Methods, and Multimodal Evidence

    • 1st Edition
    • Manuel González Canché
    • English
    Too many qualitative and mixed-methods researchers are currently being asked to make an impossible choice: either remain outside the world of advanced data science and artificial intelligence, or enter it by learning programming, relying on expensive proprietary platforms, and uploading sensitive data to external servers. This book begins from a different premise: researchers should not have to choose between rigor, accessibility, privacy, and interpretive depth. Data Science, Interactive Visualizations, and Generative AI Tools for the Analysis of Qualitative, Mixed-Methods, and Multimodal Evidence presents an integrated methodological ecosystem for ethical and equity-driven data science in qualitative and mixed-methods research. It is designed for scholars working with textual, relational, temporal, affective, spatial, visual, and multimodal evidence who want access to rigorous data science and AI-supported analytic tools without needing to master programming, pay recurring fees, or surrender control of sensitive materials.The book introduces a fully local, no-code ecosystem of software tools for analyzing complex evidence across multiple layers of inquiry—from language and structure to time, emotion, interaction, and context. Special attention is given to ISARI (Intelligent Systems for Academic Research Integration), a fully offline, open-source, multimodal brainstorming partner designed to support scholarly memoing, comparison, synthesis, and evidence-grounded writing. ISARI is presented not as a substitute for interpretation, but as part of a broader local analytic environment in which computational outputs remain accountable to researchers’ judgment and to participants’ original evidence.This is not a book about replacing researchers with AI. It is a book about giving researchers ethical, privacy-conscious, and equity-driven access to advanced analytic tools that have too often remained restricted to those with programming expertise or privileged institutional support. By bringing together interactive visualizations, machine learning, natural language processing, geocontextualization... temporal analysis, relational modeling, and local generative AI, this book offers a practical and forward-looking vision for doing rigorous research without compromising transparency, scholarly control, or data sovereignty. It is intended for researchers, faculty, graduate students, institutional analysts, and interdisciplinary scholars interested in expanding their analytic toolkit while preserving methodological accountability and interpretive authority.

  • Integrated Photonics for Sensing Applications

    • 1st Edition
    • Anu Agarwal + 2 more
    • English
    Integrated Photonics for Sensing Applications introduces sensors as an important application area for integrated photonics. It provides a brief historical perspective and highlights key free-space spectroscopic sensing techniques such as FTIR, Raman, SPR, and reflectometry, and it thoroughly explores the advantages of photonic integrated circuit (PIC) sensor systems, which have the potential for low SWAP-C and high performance.The topics covered include sensor system components such as waveguides (for optical wavelengths less than and greater than 1550 nm), ring resonators and toroids, photonic crystals, MZ interferometers, light input and output, light sources (wavelengths less than and greater than 1550 nm), and spectrometers and detectors. The book considers integrated systems that employ biofunctionalization... for applications in chem–bio sensing, and it also addresses sensor manufacturing at scale, including materials, PDK development, and sensor packaging.

  • Intelligent Cyber-Physical Systems for Sustainable Healthcare

    • 1st Edition
    • Vandana Bajaj + 5 more
    • English
    Intelligent Cyber-Physical Systems for Sustainable Healthcare addresses the integration of emerging technologies like Virtual Reality, Smart Robotics, and Human-Computer Interaction, which are crucial for maximizing digital health's potential. The book includes case studies that highlight challenges faced by medical practitioners and industry professionals, providing valuable insights for designing sustainable iCPS solutions. Aimed at biomedical engineers, researchers, and industry professionals, it offers guidance for developing practical applications in sustainable healthcare. Additionally, shared, cross-disciplinary experiences assist engineers with less clinical expertise in enhancing healthcare applications in the intelligent digital health sector. Overall, case studies illustrate past challenges in the healthcare industry and present effective solutions to overcome them, making the book a comprehensive resource for advancing sustainable healthcare practices.

  • Digital Twins

    Core Principles and AI Integration
    • 1st Edition
    • Bedir Tekinerdogan + 1 more
    • English
    Digital Twins: Core Principles, System Engineering, and AI Integration provides a comprehensive overview of digital twin technology, a cutting-edge innovation that bridges the physical and digital worlds. The book addresses common challenges such as data integration, security, scalability, and the alignment of digital twin models with actual physical processes. After presenting core concepts of digital twins for software engineering, the book discusses integration with advanced digital solutions such as AI, IoT, Cloud computing, Big Data Analytics, and Extended Reality (XR). Next, the authors provide readers with a thorough presentation of digital twins' applications in a variety of settings and industry/research topics.Finally, the book concludes with a discussion of challenges and solutions, along with future trends in digital twins research and development. As digital twin technology evolves, its integration with various advanced digital solutions is becoming essential for achieving real-time insights and autonomous decision-making. Challenges include understanding the interoperability of these technologies, managing data complexity, ensuring security, and optimizing for low-latency environments.

  • Understanding Models Developed with AI

    Including Applications with Python and MATLAB Code
    • 1st Edition
    • Ömer Faruk ErtuÄŸrul + 2 more
    • English
    Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need to build AI models, along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems.As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable.

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