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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.

  • Programming Language Pragmatics

    • 5th Edition
    • January 9, 2025
    • Michael Scott + 1 more
    • English
    Programming Language Pragmatics is the most comprehensive programming language textbook available today, with nearly 1000 pages of content in the book, plus hundreds more pages of reference materials and ancillaries online. Michael Scott takes theperspective that language design and language implementation are tightly interconnected, and that neither can be fully understood in isolation. In an approachable, readable style, he discusses more than 50 languages in the context of understanding how code isinterpreted or compiled, providing an organizational framework for learning new languages, regardless of platform. This edition has been thoroughly updated to cover the most recent developments in programming language design and provides both a solid understanding of the most important issues driving software development today

  • RISC-V System-on-Chip Design

    • 1st Edition
    • January 1, 2025
    • David Harris + 3 more
    • English
    RISC-V Microprocessor System-On-Chip Design is written to be accessible to an advanced undergraduate audience with limited background. It explains concepts from operating systems, VLSI, and memory systems as necessary, and High school mathematics is sufficient preparation for most of the book, although the floating point and division chapters will be primarily of interest to those with a curiosity about computer arithmetic. Like Harris and Harris’s Digital Design and Computer Architecture textbooks, this book will appeal to students with easy-to-read and complete explanations, sidebars, and occasional humor and cartoons.It comes with an open-source implementation and will include end-of-chapter problems to extend the RISC-V processor in various ways. Ancillary materials include a GitHub repository with complete open-source SystemVerilog code, validation code in C and assembly language, and code for benchmarking and booting Linux.

  • Machine Learning

    From the Classics to Deep Networks, Transformers, and Diffusion Models
    • 3rd Edition
    • December 6, 2024
    • Sergios Theodoridis
    • English
    Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms.Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.

  • Interdependent Human-Machine Teams

    The Path to Autonomy
    • 1st Edition
    • December 5, 2024
    • William Lawless + 3 more
    • English
    Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public.It establishes the meaning and operation of “shared contexts” between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.

  • Advances in Cancer Biomarkers Research

    • 1st Edition
    • December 1, 2024
    • Anand Narayan Singh + 2 more
    • English
    Advances in Cancer Biomarkers Research provides a thorough and detailed description of cancer biomarkers for diagnostic, prognostic and therapeutics in several cancer types. The book presents a compendium of topics related to current advanced research, along with fundamental knowledge that will help readers fully comprehend the field of cancer biomarkers. Topics discussed include such the role of genetic mechanisms, epigenetics, DNA and microRNA in different cancers, signaling pathways and exosomes. In addition, the book discusses biomarker research applied to several cancer types, such as head and neck, urological, lung, bone tumors, hematological and neurological malignancies and breast cancers.This will be a valuable resource for cancer researchers, oncologists, graduate students and members of the biomedical field who are interested in the potential of biomarkers in cancer research and treatment.

  • Systematic Review and Meta-Analysis

    Stepwise Approach for Medical and Biomedical Researchers
    • 1st Edition
    • November 29, 2024
    • Mahsa Ghajarzadeh + 2 more
    • English
    Systematic Review and Meta-Analysis: Stepwise Approach for Medical and Biomedical Researchers presents a step-by-step approach to develop systematic reviews and meta-analysis for biomedical and medical research. Systematic reviews are the highest quality evidence in medical research, providing an answer to a specific question, and they can be developed in the form of comprehensive literature search, critical appraisal, and synthesis of the evidence. Systematic reviews and meta-analysis are key elements of evidence-based medicine by summarizing the results of previous studies with the use of specific and reliable methods.Initially, systematic review and meta-analysis were exclusive to clinical trials, however systematic reviews of observational and diagnostic studies have expanded, making it essential for members of the field to master those techniques. This practical reference is a valuable resource for biostaticians, researchers, students, and members of medical and biomedical fields who need to understand more about systematic reviews and meta-analysis in their research work.

  • Exploring the Metaverse

    Challenges and Applications
    • 1st Edition
    • November 27, 2024
    • Deepika Koundal + 1 more
    • English
    Exploring the Metaverse: Challenges and Applications explores the various applications and challenges facing the metaverse, from privacy and security concerns to questions about the economy and ethical considerations. Drawing on insights from experts in technology, ethics, and economics, the book's authors provide a comprehensive overview of the metaverse and its potential implications. Through a series of engaging essays and thought-provoking case studies, they examine the complex issues facing the metaverse, such as the role of virtual identity, the impact on social interactions, and the potential for addiction. Finally, they explore potential solutions to these challenges, from technological innovations to policy interventions.

  • Trustworthy AI in Medical Imaging

    • 1st Edition
    • November 25, 2024
    • Marco Lorenzi + 1 more
    • English
    Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications.The book will help readers acquire the basic notions of AI trustworthiness and understand its concrete application in medical imaging, identify pain points and solutions to enhance trustworthiness in medical imaging applications, understand current limitations and perspectives of trustworthy AI in medical imaging, and identify novel research directions.Although the problem of trustworthiness in AI is actively researched in different disciplines, the adoption and implementation of trustworthy AI principles in real-world scenarios is still at its infancy. This is particularly true in medical imaging where guidelines and standards for trustworthiness are critical for the successful deployment in clinical practice. After setting out the technical and clinical challenges of AI trustworthiness, the book gives a concise overview of the basic concepts before presenting state-of-the-art methods for solving these challenges.

  • Fixed Point Optimization Algorithms and Their Applications

    • 1st Edition
    • November 23, 2024
    • Watcharaporn Cholamjiak
    • English
    Fixed Point Optimization Algorithms and Their Applications discusses how the relationship between fixed point algorithms and optimization problems is connected and demonstrates hands-on applications of the algorithms in fields such as image restoration, signal recovery, and machine learning. The book is divided into nine chapters beginning with foundational concepts of normed linear spaces, Banach spaces, and Hilbert spaces, along with nonlinear operators and useful lemmas and theorems for proving the book’s main results. The author presents algorithms for nonexpansive and generalized nonexpansive mappings in Hilbert space, and presents solutions to many optimization problems across a range of scientific research and real-world applications. From foundational concepts, the book proceeds to present a variety of optimization algorithms, including fixed point theories, convergence theorems, variational inequality problems, minimization problems, split feasibility problems, variational inclusion problems, and equilibrium problems. Fixed Point Optimization Algorithms and Their Applications equips readers with the theoretical mathematics background and necessary tools to tackle challenging optimization problems involving a range of algebraic methods, empowering them to apply these techniques in their research, professional work, or academic pursuits.

  • Empowering IoT with Big Data Analytics

    • 1st Edition
    • November 16, 2024
    • Mohamed Adel Serhani + 2 more
    • English
    Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics, tools, and techniques related to empowering IoT with big data technologies and big data analytics solutions, thus allowing for better processing, analysis, protection, distribution, and visualization of data for the benefit of IoT applications and second, a better deployment of IoT applications on the ground. This book covers big data in the IoT era, its application domains, current state-of-the-art in big data and IoT technologies, standards, platforms, and solutions. This book provides a holistic view of the big data value-chain for IoT, including storage, processing, protection, distribution, analytics, and visualization.Big data is a multi-disciplinary topic involving handling intensive, continuous, and heterogeneous data retrieved from different sources including sensors, social media, and embedded systems. The emergence of Internet of Things (IoT) and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data.

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