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.

Books Journals

Health Information Exchange

2nd Edition
November 9, 2022
Brian Dixon
English
Paperback
9 7 8 0 3 2 3 9 0 8 0 2 3
eBook
9 7 8 0 3 2 3 9 0 8 0 3 0

Health Information Exchange: Navigating and Managing a Network of Health Information Systems, Second Edition, now fully updated, is a practical guide on how to understand, manage and make use of a health information exchange infrastructure, which moves patient-centered information within the health care system. The book informs and guides the development of new infrastructures as well as the management of existing and expanding infrastructures across the globe. Sections explore the reasons for the health information exchange (HIE) infrastructures, how to manage them, examines the key drivers of HIE, and barriers to their widespread use. In addition, the book explains the underlying technologies and methods for conducting HIE across communities as well as nations. Finally, the book explains the principles of governing an organization that chiefly moves protected health information around. The text unravels the complexities of HIE and provides guidance for those who need to access HIE data and support operations.

Meta-Learning

1st Edition
November 5, 2022
Lan Zou
English
Paperback
9 7 8 0 3 2 3 8 9 9 3 1 4
eBook
9 7 8 0 3 2 3 9 0 3 7 0 7

Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications.

Data Analytics for Social Microblogging Platforms

1st Edition
November 4, 2022
Soumi Dutta + 3 more
English
Paperback
9 7 8 0 3 2 3 9 1 7 8 5 8
eBook
9 7 8 0 3 2 3 9 7 2 3 0 7

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Statistical Modeling in Machine Learning

1st Edition
October 29, 2022
Tilottama Goswami + 1 more
English
Paperback
9 7 8 0 3 2 3 9 1 7 7 6 6
eBook
9 7 8 0 3 2 3 9 7 2 5 2 9

Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

AI Computing Systems

1st Edition
October 12, 2022
Yunji Chen + 5 more
English
Paperback
9 7 8 0 3 2 3 9 5 3 9 9 3
eBook
9 7 8 0 3 2 3 9 5 3 9 8 6

AI Computing Systems: An Application Driven Perspective adopts the principle of "application-driven, full-stack penetration" and uses the specific intelligent application of "image style migration" to provide students with a sound starting place to learn. This approach enables readers to obtain a full view of the AI computing system. A complete intelligent computing system involves many aspects such as processing chip, system structure, programming environment, software, etc., making it a difficult topic to master in a short time.

Digital Image Enhancement and Reconstruction

1st Edition
October 6, 2022
Shyam Singh Rajput + 3 more
English
Paperback
9 7 8 0 3 2 3 9 8 3 7 0 9
eBook
9 7 8 0 3 2 3 9 8 5 7 8 9

Digital Image Enhancement and Reconstruction: Techniques and Applications explores different concepts and techniques used for the enhancement as well as reconstruction of low-quality images. Most real-life applications require good quality images to gain maximum performance, however, the quality of the images captured in real-world scenarios is often very unsatisfactory. Most commonly, images are noisy, blurry, hazy, tiny, and hence need to pass through image enhancement and/or reconstruction algorithms before they can be processed by image analysis applications. This book comprehensively explores application-specific enhancement and reconstruction techniques including satellite image enhancement, face hallucination, low-resolution face recognition, medical image enhancement and reconstruction, reconstruction of underwater images, text image enhancement, biometrics, etc. Chapters will present a detailed discussion of the challenges faced in handling each particular kind of image, analysis of the best available solutions, and an exploration of applications and future directions. The book provides readers with a deep dive into denoising, dehazing, super-resolution, and use of soft computing across a range of engineering applications.

Semantic Models in IoT and eHealth Applications

1st Edition
September 17, 2022
Sanju Tiwari + 2 more
English
Paperback
9 7 8 0 3 2 3 9 1 7 7 3 5
eBook
9 7 8 0 3 2 3 9 7 2 2 6 0

Semantic Models in IoT and eHealth Applications explores the key role of semantic web modeling in eHealth technologies, including remote monitoring, mobile health, cloud data and biomedical ontologies. The book explores different challenges and issues through the lens of various case studies of healthcare systems currently adopting these technologies. Chapters introduce the concepts of semantic interoperability within a healthcare model setting and explore how semantic representation is key to classifying, analyzing and understanding the massive amounts of biomedical data being generated by connected medical devices. Continuous health monitoring is a strong solution which can provide eHealth services to a community through the use of IoT-based devices that collect sensor data for efficient health diagnosis, monitoring and treatment. All of this collected data needs to be represented in the form of ontologies which are considered the cornerstone of the Semantic Web for knowledge sharing, information integration and information extraction.

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

Now taught in a majority of medical schools nationwide, health systems science (HSS) prepares learners for the health systems of the future—an essential topic in modern health care. Health Systems Science Education, part of the American Medical Association’s MedEd Innovation Series, is a first-of-its-kind, instructor-focused field book that that equips educators to not just teach health systems science, but to know how to integrate and implement HSS comprehensively and effectively across the curriculum. This change management-oriented volume.

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

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Applications

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Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

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Internet and web technology

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Theory and mathematics

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Books in Computer science

Health Information Exchange

Meta-Learning

Data Analytics for Social Microblogging Platforms

Statistical Modeling in Machine Learning

AI Computing Systems

Digital Image Enhancement and Reconstruction

Semantic Models in IoT and eHealth Applications

Health Systems Science Education: Development and Implementation

Health Systems Science Education: Development and Implementation

Health Systems Science Education: Development and Implementation

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