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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

    • Usability Testing Essentials: Ready, Set ...Test!

      • 2nd Edition
      • June 27, 2020
      • Carol M. Barnum
      • English
      • Paperback
        9 7 8 0 1 2 8 1 6 9 4 2 1
      • eBook
        9 7 8 0 1 2 8 1 6 9 4 3 8
      Usability Testing Essentials presents a practical, step-by-step approach to learning the entire process of planning and conducting a usability test. It explains how to analyze and apply the results and what to do when confronted with budgetary and time restrictions. This is the ideal book for anyone involved in usability or user-centered design—from students to seasoned professionals.Filled with new examples and case studies, Usability Testing Essentials, Second Edition is completely updated to reflect the latest approaches, tools and techniques needed to begin usability testing or to advance in this area.

    • Artificial Intelligence in Healthcare

      • 1st Edition
      • June 21, 2020
      • Adam Bohr + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 8 4 3 8 7
      • eBook
        9 7 8 0 1 2 8 1 8 4 3 9 4
      Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction toartificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges　and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare.

    • Human-Machine Shared Contexts

      • 1st Edition
      • June 9, 2020
      • William Lawless + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 0 5 4 3 3
      • eBook
        9 7 8 0 1 2 8 2 2 3 7 9 6
      Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of “shared contexts” between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines. This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these "machines" may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers "think through this change in human terms," the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines. This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers.

    • Practical Machine Learning for Data Analysis Using Python

      • 1st Edition
      • June 5, 2020
      • Abdulhamit Subasi
      • English
      • Paperback
        9 7 8 0 1 2 8 2 1 3 7 9 7
      • eBook
        9 7 8 0 1 2 8 2 1 3 8 0 3
      Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.

    • OpenVX Programming Guide

      • 1st Edition
      • May 22, 2020
      • Frank Brill + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 6 4 2 5 9
      • eBook
        9 7 8 0 1 2 8 1 6 6 1 9 2
      OpenVX is the computer vision API adopted by many high-performance processor vendors. It is quickly becoming the preferred way to write fast and power-efficient code on embedded systems. OpenVX Programming Guidebook presents definitive information on OpenVX 1.2 and 1.3, the Neural Network, and other extensions as well as the OpenVX Safety Critical standard. This book gives a high-level overview of the OpenVX standard, its design principles, and overall structure. It covers computer vision functions and the graph API, providing examples of usage for the majority of the functions. It is intended both for the first-time user of OpenVX and as a reference for experienced OpenVX developers.

    • Nature-Inspired Computation and Swarm Intelligence

      • 1st Edition
      • April 9, 2020
      • Xin-She Yang
      • English
      • Paperback
        9 7 8 0 1 2 8 1 9 7 1 4 1
      • eBook
        9 7 8 0 1 2 8 2 2 6 0 9 4
      Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence.

    • Manual of Engineering Drawing

      • 5th Edition
      • March 28, 2020
      • Colin H. Simmons + 2 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 8 4 8 2 0
      • eBook
        9 7 8 0 1 2 8 2 0 9 4 9 3
      Manual of Engineering Drawing: British and International Standards, Fifth Edition, chronicles ISO and British Standards in engineering drawings, providing many examples that will help readers understand how to translate engineering specifications into a visual medium. The book includes 6 introductory chapters which provide foundational theory and contextual information regarding the broader context of engineering drawing and design. The concepts enclosed will help readers gain the most out of their drawing skills. As the standards referred to in this book change every few years, this new edition presents an important update.

    • Hybrid Computational Intelligence

      • 1st Edition
      • March 5, 2020
      • Siddhartha Bhattacharyya + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 8 6 9 9 2
      • eBook
        9 7 8 0 1 2 8 1 8 7 0 0 5
      Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.

    • Durable Phase-Change Memory Architectures

      • 1st Edition
      • Volume 118
      • February 21, 2020
      • English
      • Hardback
        9 7 8 0 1 2 8 1 8 7 5 4 8
      • eBook
        9 7 8 0 1 2 8 1 8 7 5 5 5
      Advances in Computers, Volume 118, the latest volume in this innovative series published since 1960, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters in this updated release include Introduction to non-volatile memory technologies, The emerging phase-change memory, Phase-change memory architectures, Inter-line level schemes for handling hard errors in PCMs, Handling hard errors in PCMs by using intra-line level schemes, and Addressing issues with MLC Phase-change Memory.

    • The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases

      • 1st Edition
      • Volume 117
      • January 28, 2020
      • English
      • Hardback
        9 7 8 0 1 2 8 1 8 7 5 6 2
      • eBook
        9 7 8 0 1 2 8 1 8 7 5 7 9
      The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases, Volume 117, the latest volume in the Advances in Computers series, presents detailed coverage of new advancements in computer hardware, software, theory, design and applications. Chapters vividly illustrate how the emerging discipline of digital twin is strategically contributing to various digital transformation initiatives. Specific chapters cover Demystifying the Digital Twin Paradigm, Digital Twin Technology for "Smarter Manufacturing", The Fog Computing/ Edge Computing to leverage Digital Twin, The industry use cases for the Digital Twin idea, Enabling Digital Twin at the Edge, The Industrial Internet of Things (IIOT), and much more.

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