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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 Machine Vision Paradigms for Medical Image Analysis

      • 1st Edition
      • August 11, 2020
      • Tapan K. Gandhi + 4 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 9 2 9 5 5
      • eBook
        9 7 8 0 1 2 8 1 9 2 9 6 2
      Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs.

    • 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.

    • 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.

    • 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.

    • 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.

    • Trends in Deep Learning Methodologies

      • 1st Edition
      • November 12, 2020
      • Vincenzo Piuri + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 2 2 2 6 3
      • eBook
        9 7 8 0 1 2 8 2 3 2 6 8 2
      Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

    • Soft Numerical Computing in Uncertain Dynamic Systems

      • 1st Edition
      • August 19, 2020
      • Tofigh Allahviranloo + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 2 8 5 5 5
      • eBook
        9 7 8 0 1 2 8 2 2 9 9 4 1
      Soft Numerical Computing in Uncertain Dynamic Systems is intended for system specialists interested in dynamic systems that operate at different time scales. The book discusses several types of errors and their propagation, covering numerical methods—including convergence and consistence properties and characteristics—and proving of related theorems within the setting of soft computing. Several types of uncertainty representation like interval, fuzzy, type 2 fuzzy, granular, and combined uncertain sets are discussed in detail. The book can be used by engineering students in control and finite element fields, as well as all engineering, applied mathematics, economics, and computer science students. One of the important topics in applied science is dynamic systems and their applications. The authors develop these models and deliver solutions with the aid of numerical methods. Since they are inherently uncertain, soft computations are of high relevance here. This is the reason behind investigating soft numerical computing in dynamic systems. If these systems are involved with complex-uncertain data, they will be more practical and important. Real-life problems work with this type of data and most of them cannot be solved exactly and easily—sometimes they are impossible to solve. Clearly, all the numerical methods need to consider error of approximation. Other important applied topics involving uncertain dynamic systems include image processing and pattern recognition, which can benefit from uncertain dynamic systems as well. In fact, the main objective is to determine the coefficients of a matrix that acts as the frame in the image. One of the effective methods exhibiting high accuracy is to use finite differences to fill the cells of the matrix.

    • Strategy, Leadership, and AI in the Cyber Ecosystem

      • 1st Edition
      • November 10, 2020
      • Hamid Jahankhani + 4 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 1 4 4 2 8
      • eBook
        9 7 8 0 1 2 8 2 1 4 5 9 6
      Strategy, Leadership and AI in the Cyber Ecosystem investigates the restructuring of the way cybersecurity and business leaders engage with the emerging digital revolution towards the development of strategic management, with the aid of AI, and in the context of growing cyber-physical interactions (human/machine co-working relationships). The book explores all aspects of strategic leadership within a digital context. It investigates the interactions from both the firm/organization strategy perspective, including cross-functional actors/stakeholders who are operating within the organization and the various characteristics of operating in a cyber-secure ecosystem. As consumption and reliance by business on the use of vast amounts of data in operations increase, demand for more data governance to minimize the issues of bias, trust, privacy and security may be necessary. The role of management is changing dramatically, with the challenges of Industry 4.0 and the digital revolution. With this intelligence explosion, the influence of artificial intelligence technology and the key themes of machine learning, big data, and digital twin are evolving and creating the need for cyber-physical management professionals.

    • Fault-Tolerant Systems

      • 2nd Edition
      • September 1, 2020
      • Israel Koren + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 1 8 1 0 5 8
      • eBook
        9 7 8 0 1 2 8 1 8 1 0 6 5
      Fault-Tolerant Systems, Second Edition, is the first book on fault tolerance design utilizing a systems approach to both hardware and software. No other text takes this approach or offers the comprehensive and up-to-date treatment that Koren and Krishna provide. The book comprehensively covers the design of fault-tolerant hardware and software, use of fault-tolerance techniques to improve manufacturing yields, and design and analysis of networks. Incorporating case studies that highlight more than ten different computer systems with fault-tolerance techniques implemented in their design, the book includes critical material on methods to protect against threats to encryption subsystems used for security purposes. The text’s updated content will help students and practitioners in electrical and computer engineering and computer science learn how to design reliable computing systems, and how to analyze fault-tolerant computing systems.

    • 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.

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