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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

    • 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.

    • Artificial Intelligence and Data Science in Environmental Sensing

      • 1st Edition
      • February 9, 2022
      • Mohsen Asadnia + 2 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 0 5 0 8 4
      • eBook
        9 7 8 0 3 2 3 9 0 5 0 7 7
      Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications.

    • Measuring the User Experience

      • 3rd Edition
      • February 8, 2022
      • Bill Albert + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 8 0 8 0 8
      • eBook
        9 7 8 0 1 2 8 1 8 0 8 1 5
      *Textbook and Academic Authors Association (TAA) Textbook Excellence Award Winner, 2024*Measuring the User Experience: Collecting, Analyzing, and Presenting UX Metrics, Third Edition provides the quantitative analysis training that students and professionals need. This book presents an update on the first resource that focused on how to quantify user experience. Now in its third edition, the authors have expanded on the area of behavioral and physiological metrics, splitting that chapter into sections that cover eye-tracking and measuring emotion. The book also contains new research and updated examples, several new case studies, and new examples using the most recent version of Excel.

    • Deep Learning for Robot Perception and Cognition

      • 1st Edition
      • February 4, 2022
      • Alexandros Iosifidis + 1 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 5 7 8 7 1
      • eBook
        9 7 8 0 3 2 3 8 8 5 7 2 0
      Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks.

    • Deep Learning on Edge Computing Devices

      • 1st Edition
      • February 2, 2022
      • Xichuan Zhou + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 5 7 8 3 3
      • eBook
        9 7 8 0 3 2 3 9 0 9 2 7 3
      Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are introduced in each section, and a comprehensive example, smart surveillance cameras, is presented at the end of the book, integrating innovation in both algorithm and hardware architecture. Structured into three parts, the book covers core concepts, theories and algorithms and architecture optimization.This book provides a solution for researchers looking to maximize the performance of deep learning models on Edge-computing devices through algorithm-hardware co-design.

    • 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.

    • Meeting the Challenges of Data Quality Management

      • 1st Edition
      • January 25, 2022
      • Laura Sebastian-Coleman
      • English
      • Paperback
        9 7 8 0 1 2 8 2 1 7 3 7 5
      • eBook
        9 7 8 0 1 2 8 2 1 7 5 6 6
      Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.

    • Classification Made Relevant

      • 1st Edition
      • January 25, 2022
      • Jules J. Berman
      • English
      Classification Made Relevant: How Scientists Build and Use Classifications and Ontologies explains how classifications and ontologies are designed and used to analyze scientific information. The book presents the fundamentals of classification, leading up to a description of how computer scientists use object-oriented programming languages to model classifications and ontologies. Numerous examples are chosen from the Classification of Life, the Periodic Table of the Elements, and the symmetry relationships contained within the Classification Theorem of Finite Simple Groups. When these three classifications are tied together, they provide a relational hierarchy connecting all of the natural sciences. The book's chapters introduce and describe general concepts that can be understood by any intelligent reader. With each new concept, they follow practical examples selected from various scientific disciplines. In these cases, technical points and specialized vocabulary are linked to glossary items where the item is clarified and expanded.

    • Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data

      • 1st Edition
      • January 22, 2022
      • Akash Kumar Bhoi + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 5 7 5 1 2
      • eBook
        9 7 8 0 3 2 3 9 0 3 4 8 6
      Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field. The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processi... models and many memories associated with it while processing the information for forecasting future trends and decision making. This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning.

    • Machine Learning for Biometrics

      • 1st Edition
      • January 21, 2022
      • Partha Pratim Sarangi + 4 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 5 2 0 9 8
      • eBook
        9 7 8 0 3 2 3 9 0 3 3 9 4
      Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc. In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.

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