Skip to main content
Elsevier
View Cart

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

    • Meta-Learning

      • 1st Edition
      • November 5, 2022
      • Lan Zou
      • English
      • Paperback
        9 7 8 0 3 2 3 8 9 9 3 1 4
      • eBook
        9 7 8 0 3 2 3 9 0 3 7 0 7
      Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications.

    • Data Analytics for Social Microblogging Platforms

      • 1st Edition
      • November 4, 2022
      • Soumi Dutta + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 1 7 8 5 8
      • eBook
        9 7 8 0 3 2 3 9 7 2 3 0 7
      Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

    • Statistical Modeling in Machine Learning

      • 1st Edition
      • October 29, 2022
      • Tilottama Goswami + 1 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 1 7 7 6 6
      • eBook
        9 7 8 0 3 2 3 9 7 2 5 2 9
      Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

    • AI Computing Systems

      • 1st Edition
      • October 12, 2022
      • Yunji Chen + 5 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 5 3 9 9 3
      • eBook
        9 7 8 0 3 2 3 9 5 3 9 8 6
      AI Computing Systems: An Application Driven Perspective adopts the principle of "application-driven, full-stack penetration" and uses the specific intelligent application of "image style migration" to provide students with a sound starting place to learn. This approach enables readers to obtain a full view of the AI computing system. A complete intelligent computing system involves many aspects such as processing chip, system structure, programming environment, software, etc., making it a difficult topic to master in a short time.

    • Digital Image Enhancement and Reconstruction

      • 1st Edition
      • October 6, 2022
      • Shyam Singh Rajput + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 8 3 7 0 9
      • eBook
        9 7 8 0 3 2 3 9 8 5 7 8 9
      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
      • September 9, 2022
      • Rosalyn Maben-Feaster + 5 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 1 0 9 6 2
      • eBook
        9 7 8 0 4 4 3 1 1 1 4 4 0
      • eBook
        9 7 8 0 4 4 3 1 1 1 4 3 3
      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

      • 1st Edition
      • August 24, 2022
      • Haofu Liao + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 4 3 8 3 1
      • eBook
        9 7 8 0 1 2 8 2 4 4 0 3 6
      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

      • 1st Edition
      • August 21, 2022
      • Iryna Kuksa + 2 more
      • English
      • Paperback
        9 7 8 0 0 8 1 0 1 9 8 7 0
      • eBook
        9 7 8 0 0 8 1 0 1 9 8 8 7
      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
      • August 20, 2022
      • Pin-Yu Chen + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 4 0 2 0 5
      • eBook
        9 7 8 0 1 2 8 2 4 2 5 7 5
      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.

    • Engineering a Compiler

      • 3rd Edition
      • August 20, 2022
      • Keith D. Cooper + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 5 4 1 2 0
      • eBook
        9 7 8 0 1 2 8 1 8 9 2 6 9
      *Textbook and Academic Authors Association (TAA) Textbook Excellence Award Winner, 2024*Engineering a Compiler, Third Edition covers the latest developments in compiler technology, with new chapters focusing on semantic elaboration (the problems that arise in generating code from the ad-hoc syntax-directed translation schemes in a generated parser), on runtime support for naming and addressability, and on code shape for expressions, assignments and control-structures. Leading educators and researchers, Keith Cooper and Linda Torczon, have revised this popular text with a fresh approach to learning important techniques for constructing a modern compiler, combining basic principles with pragmatic insights from their own experience building state-of-the-art compilers.

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