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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

    • Advanced Data Mining Tools and Methods for Social Computing

      • 1st Edition
      • January 14, 2022
      • Sourav De + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 5 7 0 8 6
      • eBook
        9 7 8 0 3 2 3 8 5 7 0 9 3
      Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis.

    • Multicore and GPU Programming

      • 2nd Edition
      • February 9, 2022
      • Gerassimos Barlas
      • English
      • Paperback
        9 7 8 0 1 2 8 1 4 1 2 0 5
      • eBook
        9 7 8 0 1 2 8 1 4 1 2 1 2
      Multicore and GPU Programming: An Integrated Approach, Second Edition offers broad coverage of key parallel computing tools, essential for multi-core CPU programming and many-core "massively parallel" computing. Using threads, OpenMP, MPI, CUDA and other state-of-the-art tools, the book teaches the design and development of software capable of taking advantage of modern computing platforms that incorporate CPUs, GPUs and other accelerators. Presenting material refined over more than two decades of teaching parallel computing, author Gerassimos Barlas minimizes the challenge of transitioning from sequential programming to mastering parallel platforms with multiple examples, extensive case studies, and full source code. By using this book, readers will better understand how to develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting parallel machines.

    • 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.

    • 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.

    • Artificial Intelligence and Industry 4.0

      • 1st Edition
      • August 14, 2022
      • Aboul Ella Hassanien + 2 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 8 4 6 8 6
      • eBook
        9 7 8 0 3 2 3 9 0 6 3 9 5
      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.

    • Network Algorithmics

      • 2nd Edition
      • November 11, 2022
      • George Varghese + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 0 9 9 2 7 8
      • eBook
        9 7 8 0 1 2 8 0 9 9 8 6 5
      Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices, Second Edition takes an interdisciplinary approach to applying principles for efficient implementation of network devices, offering solutions to the problem of network implementation bottlenecks. In designing a network device, there are dozens of decisions that affect the speed with which it will perform – sometimes for better, but sometimes for worse. The book provides a complete and coherent methodology for maximizing speed while meeting network design goals. The book is uniquely focused on the seamless integration of data structures, algorithms, operating systems and hardware/software co-designs for high-performance routers/switches and network end systems. Thoroughly updated based on courses taught by the authors over the past decade, the book lays out the bottlenecks most often encountered at four disparate levels of implementation: protocol, OS, hardware and architecture. It then develops fifteen principles key to breaking these bottlenecks, systematically applying them to bottlenecks found in end-nodes, interconnect devices and specialty functions located along the network. Later sections discuss the inherent challenges of modern cloud computing and data center networking.

    • A Practical Approach to Interdisciplinary Complex Rehabilitation

      • 1st Edition
      • February 1, 2022
      • Cara Pelser + 2 more
      • English
      • Paperback
        9 7 8 0 7 0 2 0 8 2 7 6 4
      • eBook
        9 7 8 0 7 0 2 0 8 2 7 7 1
      An interdisciplinary team (IDT) approach is most effective in complex physical rehabilitation, but implementing a successful IDT can be challenging. This new book will help readers to understand more about the variety of professions that contribute to successful IDT working and how team members collaborate for the benefit of the rehabilitation patient and their personalised goals. This is a comprehensive, practical, evidence-based guide to complex rehabilitation from an IDT perspective, exploring the dynamic and diverse roles and challenges of the team. The fifteen chapters are written by clinicians who are highly experienced across a range of disciplines and settings, from early acute rehabilitation to community rehabilitation. A Practical Approach to Interdisciplinary Complex Rehabilitation will be an invaluable resource for all members of the team, including medical, nursing, dietetics, neuropsychiatry, occupational therapy, physiotherapy, psychology, rehabilitation coordination, speech and language therapy, and vocational rehabilitation therapy.

    • Theranostics and Precision Medicine for the Management of Hepatocellular Carcinoma, Volume 3

      • 1st Edition
      • April 15, 2022
      • Ganji Purnachandra Nagaraju + 1 more
      • English
      • Hardback
        9 7 8 0 3 2 3 9 9 2 8 3 1
      • eBook
        9 7 8 0 3 2 3 9 9 2 8 4 8
      Theranostics and Precision Medicine for the Management of Hepatocellular Carcinoma: Translational and Clinical Outcomes, Volume Three provides comprehensive information about ongoing research and clinical data on liver cancer. The book presents detailed descriptions about diagnostics and therapeutic options for easy understanding, with a focus on precision medicine approaches to improve treatment outcomes. This updated volume discusses topics such as clinical and safety assessment of HCC patients, liver transplantation as a therapeutic option, immunotherapy interventions, and image-based surveillance. In addition, it discusses immunohistology of HCC-enabled precision medicine and artificial intelligence for hepatocellular carcinomas. This is a valuable resource for cancer researchers, oncologists, graduate students, hepathologists and members of biomedical research who need to understand more about liver cancer to apply in their research work or clinical setting.

    • MATLAB

      • 6th Edition
      • May 4, 2022
      • Dorothy C. Attaway
      • English
      • Paperback
        9 7 8 0 3 2 3 9 1 7 5 0 6
      • eBook
        9 7 8 0 3 2 3 9 8 6 1 1 3
      MATLAB: A Practical Introduction to Programming and Problem Solving, winner of TAA’s 2017 Textbook Excellence Award ("Texty"), guides the reader through both programming and built-in functions to easily exploit MATLAB’s extensive capabilities for tackling engineering and scientific problems. Assuming no knowledge of programming, this book starts with programming concepts, such as variables, assignments, and selection statements, moves on to loops, and then solves problems using both the programming concept and the power of MATLAB. The sixth edition has been updated to reflect the functionality of the current version of MATLAB (R2021a), including the introduction of machine learning concepts and the Machine Learning Toolbox, and new sections on data formats and data scrubbing.

    • Adversarial Robustness for Machine Learning

      • 1st Edition
      • August 20, 2022
      • 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.

Related subjects

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