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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.

  • Computational Intelligence Applications for Text and Sentiment Data Analysis

    • 1st Edition
    • Dipankar Das + 3 more
    • English
    Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multifaceted data. The book investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text, i.e., exclusion of ‘neutral’ or ‘factual’ comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored.Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, the book's authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques play an important role in solving the inherent problems of sentiment analysis applications.

  • Is Justice Real When “Reality” is Not?

    Constructing Ethical Digital Environments
    • 1st Edition
    • Katherine B. Forrest + 1 more
    • English
    Is Justice Real When “Reality” is Not?: Constructing Ethical Digital Environments examines how frameworks and concepts of justice should evolve in virtual worlds. Directed at researchers working in, or with an interest in virtual reality, as well as those interested in the fields of artificial intelligence and justice, this book covers research regarding impacts on human psychological states existing within alternative ethical frameworks. With chapters dedicated to behavioral impacts of virtual events, robotics and "unconscious", and human psychological states of role playing and existing, readers will be well-equipped to navigate the virtual worlds in which millions of people currently spend time.

  • pH Deregulation as the Eleventh Hallmark of Cancer

    • 1st Edition
    • Tomas Koltai + 6 more
    • English
    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
    • Ismail Ben Ayed
    • English
    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

    From Fundamental Theories to Advanced Applications
    • 1st Edition
    • Chun-Wei Tsai + 1 more
    • English
    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.

  • Artificial Intelligence in Healthcare and COVID-19

    • 1st Edition
    • Parag Chatterjee + 1 more
    • English
    Artificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical and technological visions, making it even more applied. With the advent of COVID-19, it is obvious that leading universities and medical schools must include these topics and case studies in their usual courses of health informatics to keep up with the pace of technological and medical advancements. This book will also serve professors teaching courses and industry practitioners and professionals working in the R&D team of leading medical and informatics companies who want to embrace AI and eHealth to fight COVID-19. Since AI in healthcare is a comparatively new field, there exists a vacuum of literature in this field, especially when applied to COVID-19. With the area of AI in COVID-19 being quite young, students and researchers usually face a struggle to rely on the few published papers (which are obviously too specific) or whitepapers by tech-giants (which are too wide).

  • Uncertainty in Data Envelopment Analysis

    Fuzzy and Belief Degree-Based Uncertainties
    • 1st Edition
    • Farhad Hosseinzadeh Lotfi + 4 more
    • English
    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

    State-of-the-art Techniques and Applications
    • 1st Edition
    • Bernhard Preim + 3 more
    • English
    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
    • Kemal Polat + 1 more
    • English
    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

    Applications to Real-World Problems
    • 1st Edition
    • Luis Martinez Lopez + 3 more
    • English
    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.

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