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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.

  • Digital Image Enhancement and Reconstruction

    • 1st Edition
    • Shyam Singh Rajput + 3 more
    • English
    Digital Image Enhancement and Reconstruction: Techniques and Applications explores different concepts and techniques used for the enhancement as well as reconstruction of low-quality images. Most real-life applications require good quality images to gain maximum performance, however, the quality of the images captured in real-world scenarios is often very unsatisfactory. Most commonly, images are noisy, blurry, hazy, tiny, and hence need to pass through image enhancement and/or reconstruction algorithms before they can be processed by image analysis applications. This book comprehensively explores application-specific enhancement and reconstruction techniques including satellite image enhancement, face hallucination, low-resolution face recognition, medical image enhancement and reconstruction, reconstruction of underwater images, text image enhancement, biometrics, etc. Chapters will present a detailed discussion of the challenges faced in handling each particular kind of image, analysis of the best available solutions, and an exploration of applications and future directions. The book provides readers with a deep dive into denoising, dehazing, super-resolution, and use of soft computing across a range of engineering applications.

  • Health Systems Science Education: Development and Implementation

    • 1st Edition
    • Volume 4
    • Rosalyn Maben-Feaster + 5 more
    • English
    **Selected for 2026 Doody's Core Titles in Medical Education**Now taught in a majority of medical schools nationwide, health systems science (HSS) prepares learners for the health systems of the future—an essential topic in modern health care. Health Systems Science Education, part of the American Medical Association’s MedEd Innovation Series, is a first-of-its-kind, instructor-focused field book that that equips educators to not just teach health systems science, but to know how to integrate and implement HSS comprehensively and effectively across the curriculum. This change management-oriented volume.

  • Deep Network Design for Medical Image Computing

    Principles and Applications
    • 1st Edition
    • Haofu Liao + 2 more
    • English
    Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems.

  • Understanding Personalisation

    New Aspects of Design and Consumption
    • 1st Edition
    • Iryna Kuksa + 2 more
    • English
    Understanding Personalization: New Aspects of Design and Consumption addresses the global phenomenon of personalization that affects many aspects of everyday life. The book identifies the dimensions of personalization and its typologies. Issues of privacy, the ethics of design, and the designer/maker’s control versus the consumer’s freedom are covered, along with sections on digital personalization, advances in new media technologies and software development, the way we communicate, our personal devices, and the way personal data is stored and used. Other sections cover the principles of personalization and changing patterns of consumption and development in marketing that facilitate individualized products and services. The book also assesses the convergence of both producers and consumers towards the co-creation of goods and services and the challenges surrounding personalization, customization, and bespoke marketing in the context of ownership and consumption.

  • Adversarial Robustness for Machine Learning

    • 1st Edition
    • Pin-Yu Chen + 1 more
    • English
    Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems.

  • Artificial Intelligence and Industry 4.0

    • 1st Edition
    • Aboul Ella Hassanien + 2 more
    • English
    Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT.

  • Role of Tumor Microenvironment in Breast Cancer and Targeted Therapies

    • 1st Edition
    • Manzoor Ahmad Mir
    • English
    Role of Tumor Microenvironment in Breast Cancer and Targeted Therapies discusses the current understanding of breast cancer tumor microenvironment components, their role in tumorigenicity and the development of therapeutic resistance, along with updates on recent advances.

  • Up and Running with AutoCAD 2023

    2D and 3D Drawing, Design and Modeling
    • 1st Edition
    • Elliot J. Gindis + 1 more
    • English
    Up and Running with AutoCAD 2023: 2D and 3D Drawing, Design and Modeling presents a combination of step-by-step instruction, examples and insightful explanations. The book emphasizes core concepts and practical applications 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.

  • Artificial Intelligence Methods for Optimization of the Software Testing Process

    With Practical Examples and Exercises
    • 1st Edition
    • Sahar Tahvili + 1 more
    • English
    Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier...

  • Data Mining

    Concepts and Techniques
    • 4th Edition
    • Jiawei Han + 2 more
    • English
    Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.

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