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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

    • pH Deregulation as the Eleventh Hallmark of Cancer

      • 1st Edition
      • June 30, 2023
      • Tomas Koltai + 6 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 5 4 6 1 4
      • eBook
        9 7 8 0 4 4 3 1 5 4 6 2 1
      pH Deregulation as the Eleventh Hallmark of Cancer presents key concepts about pH deregulation in a concise and straight-forward manner. The book discusses topics such as pH regulation and metabolism, sodium hydrogen exchanger, monocarboxylate transporter, V-ATPase proton pump, carbonic anhydrases, and voltage gated sodium channels. In addition, it covers clinical and therapeutic implications and future perspectives. This is a valuable resource for researchers, oncologists, students and members of the biomedical and medical fields who want to learn more about the role of pH deregulation in cancer treatment. pH deregulation can improve the outcome of classical treatments without adding toxicity to them, and the book shows that treating the pH peculiarities of cancer is simple and can be performed with existing drugs. Based on the classification of tumor malignancy in ten hallmarks, the authors put pH deregulation at the spotlight and separated from metabolic reprogramming due to its impact on all other hallmarks, proposing it as an additional characteristic to evaluate and fight cancer.

    • High-Order Models in Semantic Image Segmentation

      • 1st Edition
      • June 22, 2023
      • Ismail Ben Ayed
      • English
      • Hardback
        9 7 8 0 1 2 8 0 5 3 2 0 1
      • eBook
        9 7 8 0 1 2 8 0 9 2 2 9 3
      High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging.

    • 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.

    • 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.

    • Visualization, Visual Analytics and Virtual Reality in Medicine

      • 1st Edition
      • May 15, 2023
      • Bernhard Preim + 3 more
      • English
      • Paperback
        9 7 8 0 1 2 8 2 2 9 6 2 0
      • eBook
        9 7 8 0 1 2 8 2 3 1 0 6 7
      Visualization, Visual Analytics and Virtual Reality in Medicine: State-of-the-art Techniques and Applications describes important techniques and applications that show an understanding of actual user needs as well as technological possibilities. The book includes user research, for example, task and requirement analysis, visualization design and algorithmic ideas without going into the details of implementation. This reference will be suitable for researchers and students in visualization and visual analytics in medicine and healthcare, medical image analysis scientists and biomedical engineers in general. Visualization and visual analytics have become prevalent in public health and clinical medicine, medical flow visualization, multimodal medical visualization and virtual reality in medical education and rehabilitation. Relevant applications now include digital pathology, virtual anatomy and computer-assisted radiation treatment planning.

    • 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.

    • Multi-Criteria Decision-Making Sorting Methods

      • 1st Edition
      • April 28, 2023
      • Luis Martinez Lopez + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 8 5 2 3 1 9
      • eBook
        9 7 8 0 3 2 3 8 5 2 3 2 6
      Multi Criteria Decision Making (MCDM) is a generic term for all methods that help people making decisions according to their preferences, in situations where there is more than one conflicting criterion. It is a branch of operational research dealing with finding optimal results in complex scenarios including various indicators, conflicting objectives and criteria. The approach of MCDM involves decision making concerning quantitative and qualitative factors.　　 The importance and success of MCDM are due to the fact that they have successfully dealt with different types of problematics for supporting decision makers such as choice, ranking and sorting, description.　　 Even though, each of the different problematics in MCDM is important, Multi-Criteria Decision-Making Sorting Methods will focus on sorting approaches across a wide range of interesting techniques and research disciplines. The applications which have been and can be solved by these techniques are more and more important in current real-world decision-making problems. Therefore, the book provides a clear overview of MCDM sorting methods and the different tools which can be used to solve real-world problems by revising such tools and characterizing them according to their performance and suitability for different types of problems. The book is aimed at a broad audience including computer scientists, engineers, geography and GIS experts, business and financial management experts, environment experts, and all those professional people interested in MCDM and its applications. The book may also be useful for teaching MCDM courses in fields such as industrial management, computer science, and applied mathematics, as new developments in multi-criteria decision making.

    • Reachable Sets of Dynamic Systems

      • 1st Edition
      • April 21, 2023
      • Stanislaw Raczynski
      • English
      • Paperback
        9 7 8 0 4 4 3 1 3 3 8 4 8
      • eBook
        9 7 8 0 4 4 3 1 3 3 8 3 1
      Reachable Sets of Dynamic Systems: Uncertainty, Sensitivity, and Complex Dynamics introduces differential inclusions, providing an overview as well as multiple examples of its interdisciplinary applications. The design of dynamic systems of any type is an important issue as is the influence of uncertainty in model parameters and model sensitivity. The possibility of calculating the reachable sets may be a powerful additional tool in such tasks. This book can help graduate students, researchers, and engineers working in the field of computer simulation and model building, in the calculation of reachable sets of dynamic models.

    • Robotics for Cell Manipulation and Characterization

      • 1st Edition
      • April 20, 2023
      • Changsheng Dai + 2 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 5 2 1 3 2
      • eBook
        9 7 8 0 3 2 3 9 5 2 1 4 9
      Robotics for Cell Manipulation and Characterization provides fundamental principles underpinning robotic cell manipulation and characterization, state-of-the-art technical advances in micro/nano robotics, new discoveries of cell biology enabled by robotic systems, and their applications in clinical diagnosis and treatment. This book covers several areas, including robotics, control, computer vision, biomedical engineering and life sciences using understandable figures and tables to enhance readers’ comprehension and pinpoint challenges and opportunities for biological and biomedical research.

    • Biostatistics Manual for Health Research

      • 1st Edition
      • April 19, 2023
      • Nafis Faizi + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 8 5 5 0 2
      • eBook
        9 7 8 0 4 4 3 1 8 5 5 1 9
      **Selected for Doody’s Core Titles® 2024 in Biostatistics**Biost... Manual for Health Research: A Practical Guide to Data Analysis is a guide for researchers on how to apply biostatistics on different types of data. The book approaches biostatistics and its application from medical and health researcher’s point-of-view and has real and mostly published data for practice and understanding. The interpretation and meaning of the statistical results, reporting guidelines and mistakes are taught with real world examples. This is a valuable resource for biostaticians, students and researchers from medical and biomedical fields who need to learn how to apply statistical approaches to improve their research.

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