Skip to main content
View Cart

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.

  • Emerging Practices in Telehealth

    Best Practices in a Rapidly Changing Field
    • 1st Edition
    • Andrew M. Freeman + 1 more
    • English
    **Selected for 2025 Doody’s Core Titles® in Ambulatory**Emerging Practices in Telehealth: Best Practices in a Rapidly Changing Field is an introduction to telehealth basics, best practices and implementation methods. The book guides the reader from start to finish through the workflow implementation of telehealth technology, including EMRs, clinical workflows, RPM, billing systems, and patient experience. It also explores how telehealth can increase healthcare access and decrease disparities across the globe. Practicing clinicians, medical fellows, allied healthcare professionals, hospital administrators, and hospital IT professionals will all benefit from this practical guidebook.

  • Intelligent Edge Computing for Cyber Physical Applications

    • 1st Edition
    • D. Jude Hemanth + 3 more
    • English
    Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

  • Hamiltonian Monte Carlo Methods in Machine Learning

    • 1st Edition
    • Tshilidzi Marwala + 2 more
    • English
    Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitiv... sampling parameters and high sample autocorrelation. Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.

  • Comprehensive Metaheuristics

    Algorithms and Applications
    • 1st Edition
    • Ali Mirjalili + 1 more
    • English
    Comprehensive Metaheuristics: Algorithms and Applications presents the foundational underpinnings of metaheuristics and a broad scope of algorithms and real-world applications across a variety of research fields. The book starts with fundamentals, mathematical prerequisites, and conceptual approaches to provide readers with a solid foundation. After presenting multi-objective optimization, constrained optimization, and problem formation for metaheuristics, world-renowned authors give readers in-depth understanding of the full spectrum of algorithms and techniques. Scientists, researchers, academicians, and practitioners who are interested in optimizing a process or procedure to achieve a goal will benefit from the case studies of real-world applications from different domains. The book takes a much-needed holistic approach, putting the most widely used metaheuristic algorithms together with an in-depth treatise on multi-disciplinary applications of metaheuristics. Each algorithm is thoroughly analyzed to observe its behavior, providing a detailed tutorial on how to solve problems using metaheuristics. New case studies and research problem statements are also discussed, which will help researchers in their application of the concepts.

  • Principles of Big Graph: In-depth Insight

    • 1st Edition
    • Volume 128
    • English
    Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph.

  • Data Science, Analytics and Machine Learning with R

    • 1st Edition
    • Luiz Paulo Favero + 2 more
    • English
    Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

  • Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems

    • 1st Edition
    • Bharat Bhushan + 3 more
    • English
    Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems explores the various benefits and challenges associated with the integration of blockchain with IoT healthcare systems, focusing on designing cognitive-embedded data technologies to aid better decision-making, processing and analysis of large amounts of data collected through IoT. This book series targets the adaptation of decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures, as well as big data and Internet of Things (IoT) problems can be handled in practice. Current Internet of Things (IoT) based healthcare systems are incapable of sharing data between platforms in an efficient manner and holding them securely at the logical and physical level. To this end, blockchain technology guarantees a fully autonomous and secure ecosystem by exploiting the combined advantages of smart contracts and global consensus. However, incorporating blockchain technology in IoT healthcare systems is not easy. Centralized networks in their current capacity will be incapable to meet the data storage demands of the incoming surge of IoT based healthcare wearables.

  • Mathematical Methods in Data Science

    • 1st Edition
    • Jingli Ren + 1 more
    • English
    Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

  • Intelligent Environments

    Advanced Systems for a Healthy Planet
    • 2nd Edition
    • P. Droege
    • English
    The promises and realities of digital innovation have come to suffuse everything from city regions to astronomy, government to finance, art to medicine, politics to warfare, and from genetics to reality itself. Digital systems augmenting physical space, buildings, and communities occupy a special place in the evolutionary discourse about advanced technology. The two Intelligent Environments books edited by Peter Droege span a quarter of a century across this genre. The second volume, Intelligent Environments: Advanced Systems for a Healthy Planet, asks: how does civilization approach thinking systems, intelligent spatial models, design methods, and support structures designed for sustainability, in ways that could counteract challenges to terrestrial habitability? This book examines a range of baseline and benchmark practices but also unusual and even sublime endeavors across regions, currencies, infrastructure, architecture, transactive electricity, geodesign, net-positive planning, remote work, integrated transport, and artificial intelligence in understanding the most immediate spatial setting: the human body. The result of this quest is both highly informative and useful, but also critical. It opens windows on what must fast become a central and overarching existential focus in the face of anthropogenic planetary heating and other threats—and raises concomitant questions about direction, scope, and speed of that change.

  • The Designer's Guide to the Cortex-M Processor Family

    • 3rd Edition
    • Trevor Martin
    • English
    The Designer’s Guide to the Cortex-M Microcontrollers, Third Edition provides an easy-to-understand introduction to the concepts required to develop programs in C with a Cortex-M based microcontroller. Sections cover architectural descriptions that are supported with practical examples, enabling readers to easily develop basic C programs to run on the Cortex-M0/M0+/M3 and M4 and M7 and examine advanced features of the Cortex architecture, such as memory protection, operating modes and dual stack operation. Final sections examine techniques for software testing and code reuse specific to Cortex-M microcontrollers. Users will learn the key differences between the Cortex-M0/M0+/M3 and M4 and M7; how to write C programs to run on Cortex-M based processors; how to make the best use of the CoreSight debug system; the Cortex-M operating modes and memory protection; advanced software techniques that can be used on Cortex-M microcontrollers, and much more.

Related subjects