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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.

  • Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering

    • 1st Edition
    • Goncalo Marques + 1 more
    • English
    Current Trends and Advances in Computer-Aided Intelligent Environmental Data Engineering merges computer engineering and environmental engineering. The book presents the latest finding on how data science and AI-based tools are being applied in environmental engineering research. This application involves multiple domains such as data science and artificial intelligence to transform the data collected by intelligent sensors into relevant and reliable information to support decision-making. These tools include fuzzy logic, knowledge-based systems, particle swarm optimization, genetic algorithms, Monte Carlo simulation, artificial neural networks, support vector machine, boosted regression tree, simulated annealing, ant colony algorithm, decision tree, immune algorithm, and imperialist competitive algorithm. This book is a fundamental information source because it is the first book to present the foundational reference material in this new research field. Furthermore, it gives a critical overview of the latest cross-domain research findings and technological developments on the recent advances in computer-aided intelligent environmental data engineering.

  • Cloud Computing

    Theory and Practice
    • 3rd Edition
    • Dan C. Marinescu
    • English
    Cloud Computing: Theory and Practice, Third Edition provides students and IT professionals with an in-depth analysis of the cloud from the ground up. After an introduction to network-centric computing and network-centric content, the book reviews basic concepts of concurrency and parallel and distributed systems, presents critical components of the cloud ecosystem as cloud service providers, cloud access, cloud data storage, and cloud hardware and software, covers cloud applications and cloud security, and presents research topics in cloud computing. Specific topics covered include resource virtualization, resource management and scheduling, and advanced topics like the impact of scale on efficiency, cloud scheduling subject to deadlines, alternative cloud architectures, and vehicular clouds. An included glossary covers terms grouped in several categories, from general to services, virtualization, desirable attributes and security.

  • Multicore and GPU Programming

    An Integrated Approach
    • 2nd Edition
    • Gerassimos Barlas
    • English
    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
    • Mohsen Asadnia + 2 more
    • English
    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

    Collecting, Analyzing, and Presenting UX Metrics
    • 3rd Edition
    • Bill Albert + 1 more
    • English
    *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
    • Alexandros Iosifidis + 1 more
    • English
    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

    Design Challenges of Algorithm and Architecture
    • 1st Edition
    • Xichuan Zhou + 3 more
    • English
    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.

  • Meeting the Challenges of Data Quality Management

    • 1st Edition
    • Laura Sebastian-Coleman
    • English
    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

    How Scientists Build and Use Classifications and Ontologies
    • 1st Edition
    • 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
    • Akash Kumar Bhoi + 3 more
    • English
    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.

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