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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.

  • Artificial Neural Networks and Type-2 Fuzzy Set

    Elements of Soft Computing and Its Applications
    • 1st Edition
    • February 19, 2025
    • Snehashish Chakraverty + 2 more
    • English
    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 Edition
    • February 12, 2025
    • Tuan Anh Nguyen
    • English
    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.

  • Advances in Computational Methods and Modeling for Science and Engineering

    • 1st Edition
    • February 4, 2025
    • Hari M Srivastava + 2 more
    • English
    Advances in Computational Methods and Modelling in Science and Engineering explores the application of computational techniques and modeling approaches in science and engineering, providing practical knowledge and skills for tackling complex problems using numerical simulations and data analysis. This book addresses the need for a cohesive and up-to-date resource in the rapidly evolving field of computational methods. It consolidates diverse topics, serving as a one-stop guide for individuals seeking a comprehensive understanding of the subject matter. Sections focus on mathematical techniques that provide global solutions for models arising in engineering and scientific research applications by considering their long-term benefits.The mathematical treatment of these models is very helpful in understanding these models and their real-world applications. The methods and modeling techniques presented are useful for mathematicians, engineers, scientists, and researchers working on the mathematical treatment of models in a wide range of applications, including disciplines such as engineering, physics, chemistry, computer science, and applied mathematics.

  • Dimensionality Reduction in Machine Learning

    • 1st Edition
    • February 4, 2025
    • Jamal Amani Rad + 2 more
    • English
    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.

  • Data Mining

    Practical Machine Learning Tools and Techniques
    • 5th Edition
    • February 4, 2025
    • Ian H. Witten + 4 more
    • English
    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

  • The Core Network for 5G Advanced

    • 2nd Edition
    • February 3, 2025
    • Stefan Rommer + 5 more
    • English
    The Core Network for 5G Advanced, Second Edition covers up to the 3GPP release 17 & 18 which includes the core network for 5G Advanced as well as a large number of new features added by 3GPP to expand the initial 5G Core specifications in rel-15/16. This new release includes A complete update to reflect developments in Releases 17 and 18, along with new chapters on Service Exposure, non-3GPP access, 3GPP access for new use cases, Edge computing, Industry and Enterprise features, Regulatory services and Network automation and AI/ML, an overview of the 5G Core Architecture, and much more.Examples of 5G network deployment options for different use cases are also included. Written by authors who are heavily involved in the development of 5G standards, and who have written several successful books on 4G and 5G Core Networks, this book provides an authoritative reference on the technologies and standards of the 3GPP 5G Core network.

  • Blockchain and Digital Twin for Smart Hospitals

    • 1st Edition
    • February 1, 2025
    • Tuan Anh Nguyen
    • English
    Blockchain and Digital Twins for Smart Healthcare describes the role of blockchain and digital twins in smart healthcare, covering 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. Medical data is collected real-time from devices and systems in 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.

  • Fractional Modeling of Fluid Flow and Transport Phenomena

    • 1st Edition
    • January 31, 2025
    • Mohamed F. El-Amin
    • English
    Fractional Modeling of Fluid Flow and Transport Phenomena focuses on mathematical and numerical aspects of fractional-order modeling in fluid flow and transport phenomena. The book covers fundamental concepts, advancements, and practical applications, including modeling developments, numerical solutions, and convergence analysis for both time and space fractional order models. Various types of flows are explored, such as single- and multi-phase flows in porous media, involving different fluid types like Newtonian, non-Newtonian, nanofluids, and ferrofluids. This book serves as a comprehensive reference on fractional-order modeling of fluid flow and transport phenomena, offering a single resource that is currently unavailable.Fraction... modeling has gained traction in engineering and science, particularly in fluid dynamics and transport phenomena. However, its mathematical and numerical advancements have progressed relatively slowly compared to other aspects. Therefore, this book emphasizes the fractional-order modeling of fluid flow and transport phenomena to bridge this gap. Each chapter in the book delves into a specific topic closely related to the others, ensuring a cohesive and self-contained structure.

  • Advanced Sensors for Smart Healthcare

    • 1st Edition
    • January 27, 2025
    • Tuan Anh Nguyen
    • English
    Advanced Sensors for Smart Healthcare provides an invaluable resource for researchers and healthcare practitioners who are eager to use technology to improve the lives of patients. Sections highlight data from sensor networks via the smart hospital framework, including data, insights, and access. This book shows how the use of sensors to gather data on a patient's condition and the environment their care takes place in can allow healthcare professionals to monitor well-being and make informed decisions about treatment.

  • Applied Graph Data Science

    Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
    • 1st Edition
    • January 27, 2025
    • Pethuru Raj + 4 more
    • English
    Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

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