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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

    • Practical Design and Applications of Medical Devices

      • 1st Edition
      • November 24, 2023
      • Dilber Uzun Ozsahin + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 4 1 3 3 1
      • eBook
        9 7 8 0 4 4 3 1 4 1 3 2 4
      Practical Design and Applications of Medical Devices focuses on devices which receive and transmit bioelectric signals, such as electrocardiograph, electrodes, blood flow, blood pressure, physiological effects and, in some cases, current flowing through the human body. This thorough guide for researchers and engineers in the field of biomedical and instrumentation engineering presents a streamlined medical strategy for designing these medical devices, sensors, and tools. It also promotes operational efficiency in the healthcare industry, with the goals of improving patient safety, lowering overall healthcare costs, broadening access to healthcare services, and improving accessibility.

    • Nitric Oxide in Health and Disease

      • 1st Edition
      • April 13, 2023
      • Lucia Morbidelli + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 3 4 2 8
      • eBook
        9 7 8 0 4 4 3 1 3 3 4 3 5
      Approx.326 pages

    • Handbook of Metaheuristic Algorithms

      • 1st Edition
      • May 30, 2023
      • Chun-Wei Tsai + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 9 1 0 8 4
      • eBook
        9 7 8 0 4 4 3 1 9 1 0 9 1
      Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains. Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.

    • Computational Methods in Drug Discovery and Repurposing for Cancer Therapy

      • 1st Edition
      • March 22, 2023
      • Ganji Purnachandra Nagaraju + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 5 2 8 0 1
      • eBook
        9 7 8 0 4 4 3 1 5 2 8 1 8
      Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability. The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others. This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.

    • Uncertainty in Data Envelopment Analysis

      • 1st Edition
      • May 19, 2023
      • Farhad Hosseinzadeh Lotfi + 4 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 9 4 4 4 6
      • eBook
        9 7 8 0 3 2 3 9 9 4 4 5 3
      Classical data envelopment analysis (DEA) models use crisp data to measure the inputs and outputs of a given system. In cases such as manufacturing systems, production processes, service systems, etc., the inputs and outputs may be complex and difficult to measure with classical DEA models. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex uncertain data, then they will become more important and practical for decision makers.Uncertainty in Data Envelopment Analysis introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods, fuzzy DEA and belief degree-based uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where the inputs and outputs of systems and processes are volatile and complex, making measurement difficult.

    • Hamiltonian Monte Carlo Methods in Machine Learning

      • 1st Edition
      • February 3, 2023
      • Tshilidzi Marwala + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 9 0 3 5 3
      • eBook
        9 7 8 0 4 4 3 1 9 0 3 6 0
      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.

    • Diagnostic Biomedical Signal and Image Processing Applications With Deep Learning Methods

      • 1st Edition
      • April 30, 2023
      • Kemal Polat + 1 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 6 1 2 9 5
      • eBook
        9 7 8 0 3 2 3 9 9 6 8 1 5
      Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods presents comprehensive research on both medical imaging and medical signals analysis. The book discusses classification, segmentation, detection, tracking and retrieval applications of non-invasive methods such as EEG, ECG, EMG, MRI, fMRI, CT and X-RAY, amongst others. These image and signal modalities include real challenges that are the main themes that medical imaging and medical signal processing researchers focus on today. The book also emphasizes removing noise and specifying dataset key properties, with each chapter containing details of one of the medical imaging or medical signal modalities. Focusing on solving real medical problems using new deep learning and CNN approaches, this book will appeal to research scholars, graduate students, faculty members, R&D engineers, and biomedical engineers who want to learn how medical signals and images play an important role in the early diagnosis and treatment of diseases.

    • Intelligent Edge Computing for Cyber Physical Applications

      • 1st Edition
      • February 3, 2023
      • D. Jude Hemanth + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 9 4 1 2 5
      • eBook
        9 7 8 0 3 2 3 9 9 4 3 3 0
      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.

    • Machine Learning for Biomedical Applications

      • 1st Edition
      • September 7, 2023
      • Maria Deprez + 1 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 2 9 0 4 0
      • eBook
        9 7 8 0 1 2 8 2 2 9 0 5 7
      Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.

    • Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems

      • 1st Edition
      • November 21, 2023
      • Yuekuan Zhou + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 1 7 7 6
      • eBook
        9 7 8 0 4 4 3 1 3 1 7 8 3
      Advances in Digitalization and Machine Learning for Integrated Building-Transportat... Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy lifecycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportat... energy systems. This title provides critical information to students, researchers, and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity.

Related subjects

Applications

Artificial intelligence

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Computer vision and pattern recognition

Computing milieux

Data

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Theory and mathematics