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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.

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161-170 of 2559 results in All results

Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems

  • 1st Edition
  • January 10, 2023
  • Bharat Bhushan + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 1 9 9 - 5
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 3 9 1 - 3
Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems explores the various benefits and challenges associated with the integration of blockchain with IoT healthcare systems, focusing on designing cognitive-embedded data technologies to aid better decision-making, processing and analysis of large amounts of data collected through IoT. This book series targets the adaptation of decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures, as well as big data and Internet of Things (IoT) problems can be handled in practice. Current Internet of Things (IoT) based healthcare systems are incapable of sharing data between platforms in an efficient manner and holding them securely at the logical and physical level. To this end, blockchain technology guarantees a fully autonomous and secure ecosystem by exploiting the combined advantages of smart contracts and global consensus. However, incorporating blockchain technology in IoT healthcare systems is not easy. Centralized networks in their current capacity will be incapable to meet the data storage demands of the incoming surge of IoT based healthcare wearables.

Mathematical Methods in Data Science

  • 1st Edition
  • January 6, 2023
  • Jingli Ren + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 8 6 7 9 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 8 6 8 0 - 6
Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

Intelligent Environments

  • 2nd Edition
  • December 5, 2022
  • P. Droege
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 0 2 4 7 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 0 2 4 8 - 7
The promises and realities of digital innovation have come to suffuse everything from city regions to astronomy, government to finance, art to medicine, politics to warfare, and from genetics to reality itself. Digital systems augmenting physical space, buildings, and communities occupy a special place in the evolutionary discourse about advanced technology. The two Intelligent Environments books edited by Peter Droege span a quarter of a century across this genre. The second volume, Intelligent Environments: Advanced Systems for a Healthy Planet, asks: how does civilization approach thinking systems, intelligent spatial models, design methods, and support structures designed for sustainability, in ways that could counteract challenges to terrestrial habitability? This book examines a range of baseline and benchmark practices but also unusual and even sublime endeavors across regions, currencies, infrastructure, architecture, transactive electricity, geodesign, net-positive planning, remote work, integrated transport, and artificial intelligence in understanding the most immediate spatial setting: the human body. The result of this quest is both highly informative and useful, but also critical. It opens windows on what must fast become a central and overarching existential focus in the face of anthropogenic planetary heating and other threats—and raises concomitant questions about direction, scope, and speed of that change.

The Designer's Guide to the Cortex-M Processor Family

  • 3rd Edition
  • December 2, 2022
  • Trevor Martin
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 5 4 9 4 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 5 4 9 5 - 5
The Designer’s Guide to the Cortex-M Microcontrollers, Third Edition provides an easy-to-understand introduction to the concepts required to develop programs in C with a Cortex-M based microcontroller. Sections cover architectural descriptions that are supported with practical examples, enabling readers to easily develop basic C programs to run on the Cortex-M0/M0+/M3 and M4 and M7 and examine advanced features of the Cortex architecture, such as memory protection, operating modes and dual stack operation. Final sections examine techniques for software testing and code reuse specific to Cortex-M microcontrollers. Users will learn the key differences between the Cortex-M0/M0+/M3 and M4 and M7; how to write C programs to run on Cortex-M based processors; how to make the best use of the CoreSight debug system; the Cortex-M operating modes and memory protection; advanced software techniques that can be used on Cortex-M microcontrollers, and much more.

Digital Twin for Healthcare

  • 1st Edition
  • November 21, 2022
  • Abdulmotaleb El Saddik
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 1 6 3 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 5 0 9 5 - 4
Digital Twins for Healthcare: Design, Challenges and Solutions establishes the state-of-art in the specification, design, creation, deployment and exploitation of digital twins' technologies for healthcare and wellbeing. A digital twin is a digital replication of a living or non-living physical entity. When data is transmitted seamlessly, it bridges the physical and virtual worlds, thus allowing the virtual entity to exist simultaneously with the physical entity. A digital twin facilitates the means to understand, monitor, and optimize the functions of the physical entity and provide continuous feedback. It can be used to improve citizens' quality of life and wellbeing in smart cities and the virtualization of industrial processes.

Network Algorithmics

  • 2nd Edition
  • November 11, 2022
  • George Varghese + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 9 9 2 7 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 9 9 8 6 - 5
Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices, Second Edition takes an interdisciplinary approach to applying principles for efficient implementation of network devices, offering solutions to the problem of network implementation bottlenecks. In designing a network device, there are dozens of decisions that affect the speed with which it will perform – sometimes for better, but sometimes for worse. The book provides a complete and coherent methodology for maximizing speed while meeting network design goals. The book is uniquely focused on the seamless integration of data structures, algorithms, operating systems and hardware/software co-designs for high-performance routers/switches and network end systems. Thoroughly updated based on courses taught by the authors over the past decade, the book lays out the bottlenecks most often encountered at four disparate levels of implementation: protocol, OS, hardware and architecture. It then develops fifteen principles key to breaking these bottlenecks, systematically applying them to bottlenecks found in end-nodes, interconnect devices and specialty functions located along the network. Later sections discuss the inherent challenges of modern cloud computing and data center networking.

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.

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