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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.

BooksJournals

Agent-Based Models with MATLAB

  • 1st Edition
  • January 1, 2025
  • Erik Cuevas + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 0 0 4 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 0 0 5 - 8
Agent-Based Models with MATLAB introduces Agent-Based Modeling (ABM), one of the most important methodologies for complex systems modeling. The book explores computational implementations and accompanying MATLAB software code as a means of inspiring readers to apply agent-based models to solve a diverse range of problems. It comes with a large amount of software code that accompanies the main text, and the modeling systems described in the book are implemented using MATLAB as the programming language. Despite the heavy mathematical components of Agent-Based Models and complex systems, it is possible to utilize these models without in-depth understanding of their mathematical fundamentals.This book enables computer scientists, mathematicians, researchers, and engineers to apply ABM in a wide range of research and engineering applications. It gradually advances from basic to more advanced methods while reinforcing complex systems through practical, hands-on applications of various computational models.

Machine Learning

  • 3rd Edition
  • December 6, 2024
  • Sergios Theodoridis
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 2 3 8 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 2 3 9 - 2
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.New to this editionThe new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models.

Interdependent Human-Machine Teams

  • 1st Edition
  • December 5, 2024
  • William Lawless + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 2 4 6 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 2 4 7 - 7
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
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 5 2 5 8 - 3
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 5 2 5 9 - 0
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

  • 1st Edition
  • November 29, 2024
  • Mahsa Ghajarzadeh + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 4 2 8 - 9
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 4 2 7 - 2
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

  • 1st Edition
  • November 27, 2024
  • Deepika Koundal + 1 more
  • English
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 1 3 1 - 4
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
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 3 7 6 1 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 3 7 6 0 - 7
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
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 3 5 8 6 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 3 5 8 7 - 7
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 author presents algorithms for non-expansive and generalized non-expansive mappings in Hilbert space, and includes 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.This book will equip 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
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 6 4 0 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 6 4 1 - 1
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.

Biomedical Robots and Devices in Healthcare

  • 1st Edition
  • November 14, 2024
  • Faiz Iqbal + 3 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 3 - 2 2 2 0 6 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 2 0 7 - 8
Biomedical Robots and Devices in Healthcare: Opportunities and Challenges for Future Applications explores recent advances and challenges involved in using these techniques in healthcare and biomedical engineering, offering insights and guidance to researchers, professionals, and graduate students interested in this area. The book covers key topics such as the current state-of-the-art in biomedical robotics and devices, the role of emerging technologies like artificial intelligence and machine learning, rehabilitation robotics, and the optimization techniques for optimal design and control. The book concludes by exploring the potential future developments and trends in the field of biomedical robotics and devices and their healthcare implications.

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