Skip to main content
header:cart.shoppingCart

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.

  • Federated Learning for Medical Imaging

    Principles, Algorithms, and Applications
    • 1st book:metaData.edition
    • Xiaoxiao Li contributors.plusContributors
    • publicationLanguages:en
    Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging.

  • Deep Learning in Action: Image and Video Processing for Practical Use

    • 1st book:metaData.edition
    • Abdussalam Elhanashi contributors.plusContributors
    • publicationLanguages:en
    Artificial intelligence technology has entered an extraordinary phase of fast development and wide application. The techniques developed in traditional AI research areas, such as computer vision and object recognition, have found many innovative applications in an array of real-world settings. The general methodological contributions from AI, such as a variety of recently developed deep learning algorithms, have also been applied to a wide spectrum of fields such as surveillance applications, real-time processing, IoT devices, and health care systems. The state-of-the-art and deep learning models have wider applicability and are highly efficient. Deep Learning in Action: Image and Video Processing for Practical Use provides a comprehensive and accessible resource for both intermediate to advanced readers seeking to harness the power of deep learning in the domains of video and image processing. The book bridges the gap between theoretical concepts and practical implementation by emphasizing lightweight approaches, enabling readers to efficiently apply deep learning techniques to real-world scenarios. It focuses on resource-efficient methods, making it particularly relevant in contexts where computational constraints are a concern.

  • Quantum Process Algebra

    • 1st book:metaData.edition
    • Yong Wang
    • publicationLanguages:en
    Quantum Process Algebra introduces readers to the algebraic properties and laws for quantum computing. The book provides readers with all aspects of algebraic theory for quantum computing, including the basis of semantics and axiomatization for quantum computing. With the assumption of a quantum system, readers will learn to solve the modeling of the three main components in a quantum system: the unitary operator, quantum measurement, and quantum entanglement, with full support of quantum and classical computing in closed systems. Next, the book establishes the relationship between probabilistic quantum bisimilarity and classical probabilistic bisimilarity, including strong probabilistic bisimilarity and weak probabilistic bisimilarity, which makes an axiomatization of quantum processes possible. With this framework, quantum and classical computing mixed processes are unified with the same structured operational semantics. Finally, the book establishes a series of axiomatizations of quantum process algebras. These process algebras support nearly all the main computation properties. Quantum and classical computing in closed quantum systems are unified with the same equational logic and the same structured operational semantics under the framework of ACP-like probabilistic process algebra. This unification means that the mathematics in the book can be used widely for verification of quantum and classical computing mixed systems, for example, most quantum communication protocols. ACP-like axiomatization also inherits the advantages of ACP, for example, and modularity means that it can be extended in an elegant way.

  • Necrobotics for Healthcare Applications and Management

    • 1st book:metaData.edition
    • Hemachandran Kannan contributors.plusContributors
    • publicationLanguages:en
    Necrobotics for Healthcare Applications and Management delves into the emerging area of necrobotics and its implications for healthcare. Exploring the convergence of robotics, technology, and healthcare, the book presents leading-edge research, practical implementations, and ethical considerations. It bridges a significant gap in healthcare literature, furnishing a contemporary and comprehensive perspective on necrobotics. Highlighting its distinct applications, management nuances, and ethical dimensions in the domain of medical robotics, the book equips readers with an in-depth grasp of this evolving field. It offers insights into technological intricacies, practical utilization, and ethical guidelines. Through real-world case studies and exemplar practices, it vividly demonstrates successful necrobotics deployments while addressing integration challenges. The book facilitates adept navigation of necrobotics complexities, spur innovation, enhance patient outcomes, and contribute to healthcare evolution. Catering to the distinct information requisites and daily obstacles encountered by engineers, healthcare practitioners, and researchers, the book offers extensive insights into necrobotics technologies, real-life case studies, and ethical reflections. It stands as a valuable resource for individuals striving to harness necrobotics' potential for efficacious healthcare solutions.

  • Digital Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City

    • 1st book:metaData.edition
    • Tuan Anh Nguyen
    • publicationLanguages:en
    In smart cities, information and communication technologies are integrated to exchange real-time data between citizens, governments, and organizations. Blockchain provides security for communication and transactions between multiple stakeholders. Digital twin refers to a simulation of physical products in a virtual space. This simulation fully utilizes the physical models, wireless sensor networks, and historical data of city operation to integrate big information (digital twin cities) under multidiscipline, multiphysical quantities, multiscale, and multiprobability.Dig... Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City explores how digital twins and blockchain can be used in smart cities. Part 1 deals with their promising applications for healthy cities. Part 2 covers other promising applications and current perspectives of blockchain and digital twins for future smart society and smart city mobility. Together with its companion volume, Digital Twin and Blockchain for Sensor Networks in Smart Cities, this book helps to understand the vast amount of data around the city to encourage happy, healthy, safe, and productive lives.

  • Digital Twin and Blockchain for Sensor Networks in Smart Cities

    • 1st book:metaData.edition
    • Tuan Anh Nguyen
    • publicationLanguages:en
    Digital twin, blackchain, and wireless sensor networks can work together to improve services in the smart city. Big data derived from wireless sensor networks can be integrated to accommodate the exchange of real-time data between citizens, governments, and organizations. Blockchain can provide high security for large-scale communications and transactions between many stakeholders. Digital twin uses physical models and historical data to integrate big information under multidiscipline, multiphysical quantities, multiscale, and multiprobability conditions.Digital Twin and Blockchain for Sensor Networks in Smart Cities explores how digital twin and blockchain can be optimized to improve services. This book is divided into three parts. Part 1 focuses on the fundamental concepts of blockchain and digital twin for sensor networks in the smart cities, while Part 2 describes their applications for managing the regular operations and services. Part 3 deals with their applications for safe cities.

  • Artificial Neural Networks and Type-2 Fuzzy Set

    Elements of Soft Computing and Its Applications
    • 1st book:metaData.edition
    • Snehashish Chakraverty contributors.plusContributors
    • publicationLanguages:en
    Soft computing is an emerging discipline which aims to exploit tolerance for imprecision, approximate reasoning, and uncertainty to achieve robustness, tractability, and cost effectiveness for building intelligent machines. Soft computing methodologies include neural networks, fuzzy sets, genetic algorithms, Bayesian networks, and rough sets, among others. In this regard, neural networks are widely used for modeling dynamic solvers, classification of data, and prediction of solutions, whereas fuzzy sets provide a natural framework for dealing with uncertainty. Artificial Neural Networks and Type-2 Fuzzy Set: Elements of Soft Computing and Its Applications covers the fundamental concepts and the latest research on variants of Artificial Neural Networks (ANN), including scientific machine learning and Type-2 Fuzzy Set (T2FS). In addition, the book also covers different applications for solving real-world problems along with various examples and case studies. It may be noted that quite a bit of research has been done on ANN and Fuzzy Set theory/ Fuzzy logic. However, Artificial Neural Networks and Type-2 Fuzzy Set is the first book to cover the use of ANN and fuzzy set theory with regards to Type-2 Fuzzy Set in static and dynamic problems in one place. Artificial Neural Networks and Type-2 Fuzzy Sets are two of the most widely used computational intelligence techniques for solving complex problems in various domains. Both ANN and T2FS have unique characteristics that make them suitable for different types of problems. This book provides the reader with in-depth understanding of how to apply these computational intelligence techniques in various fields of science and engineering in general and static and dynamic problems in particular. Further, for validation purposes of the ANN and fuzzy models, the obtained solutions of each model in the book is compared with already existing solutions that have been obtained with numerical or analytical methods.

  • Blockchain and Digital Twin for Smart Healthcare

    • 1st book:metaData.edition
    • Tuan Anh Nguyen
    • publicationLanguages:en
    The smart hospital framework involves three main layers: data, insight and access. Medical data is collected real-time from devices and systems in a smart hospitals: the internet of medical things. This data is integrated to provide insight from the analytics or machine learning software using digital twins. Security and transparency are brought through a combination of digital twin and blockchain technologies. Blockchain and Digital Twins for Smart Healthcare describes the role of blockchain and digital twins in smart healthcare. It describes the ecosystem of the Internet of Medical Things, how data can be gathered using a sensor network, which is securely stored, updated and managed with blockchain for efficient and private medical data exchange. The end goal is insight that provides faster, smarter decisions with more efficiency to improve care for the patient.

  • Data Mining

    Practical Machine Learning Tools and Techniques
    • 5th book:metaData.edition
    • Ian H. Witten contributors.plusContributors
    • publicationLanguages:en
    **2026 Textbook and Academic Authors Association (TAA) Textbook Excellence "Texty" Award Winner**Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research

  • Dimensionality Reduction in Machine Learning

    • 1st book:metaData.edition
    • Jamal Amani Rad contributors.plusContributors
    • publicationLanguages:en
    Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.

Related subjects