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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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21-30 of 2576 results in All results

Intelligent and Soft Computing

  • 1st Edition
  • February 1, 2025
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 0 0 9 0 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 0 0 9 1 - 2
Intelligent and Soft Computing: Techniques and Applications illustrates the synergistic effect of cutting-edge developments in all areas of both fields to build intelligent software systems to solve a multitude of problems. The book offers an introduction to artificial intelligence as well as fuzzy logic concepts, systems, and methods. Additional sections focus on AI tools and applications, search and data mining, reasoning and evolution, as well as nature-inspired computing techniques. Applications and case studies are included in all chapters, making this an ideal resource for researchers, graduate students, and professionals working in these domains.Readers will find this to be a timely reference on current and future developments in this emerging domain for decision support, management, information technology, and business applications.

Blockchain and Digital Twin for Smart Healthcare

  • 1st Edition
  • February 1, 2025
  • Tuan Anh Nguyen
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 0 3 0 0 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 0 3 0 1 - 2
The smart hospital framework involves three main layers: data, insight and access. Medical data is collected real-time from devices and systems in a smart hospitals: the internet of medical things. This data is integrated to provide insight from the analytics or machine learning software using digital twins. Security and transparency are brought through a combination of digital twin and blockchain technologies. Blockchain and Digital Twins for Smart Healthcare describes the role of blockchain and digital twins in smart healthcare. It describes the ecosystem of the Internet of Medical Things, how data can be gathered using a sensor network, which is securely stored, updated and managed with blockchain for efficient and private medical data exchange. The end goal is insight that provides faster, smarter decisions with more efficiency to improve care for the patient.

The Digital Doctor

  • 1st Edition
  • January 10, 2025
  • Chayakrit Krittanawong
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 7 2 8 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 4 3 4 4 - 5
The Digital Doctor: How Digital Health Can Transform Healthcare discusses digital health and demonstrates the appropriateness of each technology using an evidence-based approach. It serves as a comprehensive summary on current, evidence-based digital health applications, future novel digital health technologies (e.g., mobile health, blockchain, web3.0), as well as some of the current challenges and future directions for digital health within the various medical subspecialties. This book is a comprehensive review of digital health for clinicians, researchers, bioinformatic students, biomedical engineers interested in this topic.

RISC-V Microprocessor System-On-Chip Design

  • 1st Edition
  • January 1, 2025
  • David Harris + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 4 9 8 - 9
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 4 9 9 - 6
RISC-V Microprocessor System-On-Chip Design is written to be accessible to an advanced undergraduate audience with limited background. It explains concepts from operating systems, VLSI, and memory systems as necessary, and High school mathematics is sufficient preparation for most of the book, although the floating point and division chapters will be primarily of interest to those with a curiosity about computer arithmetic. Like Harris and Harris’s Digital Design and Computer Architecture textbooks, this book will appeal to students with easy-to-read and complete explanations, sidebars, and occasional humor and cartoons.It comes with an open-source implementation and will include end-of-chapter problems to extend the RISC-V processor in various ways. Ancillary materials include a GitHub repository with complete open-source SystemVerilog code, validation code in C and assembly language, and code for benchmarking and booting Linux.

Computer Architecture

  • 7th Edition
  • January 1, 2025
  • John L. Hennessy + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 4 0 6 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 4 0 7 - 2
Computer Architecture: A Quantitative Approach, has been considered essential reading by instructors, students and practitioners of computer design for nearly 30 years. The seventh edition of this classic textbook from John Hennessy and David Patterson, w

Agent-Based Models with MATLAB

  • 1st Edition
  • January 1, 2025
  • Erik Cuevas + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 0 0 4 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 0 0 5 - 8
As the world becomes more complicated and linked, many of our research questions can no longer be answered using straightforward models. Agent-Based Models with MATLAB introduces one of the most important methodologies for complex systems modeling, called Agent-Based Modeling (ABM), using computational implementations and accompanying MATLAB software code as a means of inspiring readers to apply agent-based models to solve a diverse range of problems. Observing the implementation of a particular approach through code can be helpful for readers, even those with strong mathematical abilities, as it eliminates ambiguities and uncertainties, making the material easier to grasp and communicate. The book comes with a large amount of software code accompanying the main text, and the modeling systems described in the book are implemented using MATLAB as the programming language. Despite the heavy mathematical components of Agent-Based Models and complex systems, it is possible to utilize these models without in-depth understanding of their mathematical fundamentals. For many readers, a more feasible goal is to grasp the concepts of these models through programming instead of mathematical formulations. Agent-Based Models with MATLAB enables computer scientists, mathematicians, researchers and engineers to apply ABM in a wide range of research and engineering applications. The book gradually advances from basic to more advanced methods, while reinforcing understanding of complex systems through practical, hands-on applications of various computational models.

Advanced Sensors for Smart Healthcare

  • 1st Edition
  • January 1, 2025
  • Tuan Anh Nguyen
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 7 9 0 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 7 9 1 - 0
Data from sensor networks via the smart hospital framework is comprised of three main layers: data, insight and access. Medical data is collected in real-time from an array of intelligent devices/systems deployed within the hospital. This data offers insight from the analytics or machine learning software that is accessible to healthcare staff via a smartphone or mobile device to facilitate swifter decisions and greater efficiency. Advanced Sensors for Smart Healthcare shows how the use of sensors to gather data on a patient's condition and the environment in which their care takes place can allow healthcare professionals to monitor their well-being and make informed decisions about their treatment. Written by experts in the field, Advanced Sensors for Smart Healthcare, sits alongside companion volume, Sensor Networks for Smart Hospitals, and is an invaluable resource for researchers and healthcare practitioners in their drive to use technology to improve the lives of patients.

Probability for Deep Learning Quantum

  • 1st Edition
  • January 1, 2025
  • Charles R. Giardina
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 8 3 4 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 8 3 5 - 1
Probability for Deep Learning Quantum provides readers with the first book to address probabilistic methods in the deep learning environment and the quantum technological area simultaneously, by using a common platform: the Many-Sorted Algebra (MSA) view. While machine learning is created with a foundation of probability, probability is at the heart of quantum physics as well. It is the cornerstone in quantum applications. These applications include quantum measuring, quantum information theory, quantum communication theory, quantum sensing, quantum signal processing, quantum computing, quantum cryptography, and quantum machine learning. Although some of the probabilistic methods differ in machine learning disciplines from those in the quantum technologies, many techniques are very similar.Probability is introduced in the text rigorously, in Komogorov’s vision. It is however, slightly modified by developing the theory in a Many-Sorted Algebra setting. This algebraic construct is also used in showing the shared structures underlying much of both machine learning and quantum theory. Both deep learning and quantum technologies have several probabilistic and stochastic methods in common. These methods are described and illustrated using numerous examples within the text. Concepts in entropy are provided from a Shannon as well as a von-Neumann view. Singular value decomposition is applied in machine learning as a basic tool and presented in the Schmidt decomposition. Besides the in-common methods, Born’s rule as well as positive operator valued measures are described and illustrated, along with quasi-probabilities. Author Charles R. Giardina provides clear and concise explanations, accompanied by insightful and thought-provoking visualizations, to deepen your understanding and enable you to apply the concepts to real-world scenarios.

Professional Penetration Testing

  • 3rd Edition
  • January 1, 2025
  • Thomas Wilhelm
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 6 4 7 8 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 6 4 7 9 - 5
Professional Penetration Testing: Creating and Learning in a Hacking Lab, Third Edition walks the reader through the entire process of setting up and running a pen test lab. Penetration testing—the act of testing a computer network to find security vulnerabilities before they are maliciously exploited—is a crucial component of information security in any organization. Chapters cover planning, metrics, and methodologies, the details of running a pen test, including identifying and verifying vulnerabilities, and archiving, reporting and management practices. The material presented will be useful to beginners through advanced practitioners.Here, author Thomas Wilhelm has delivered penetration testing training to countless security professionals, and now through the pages of this book, the reader can benefit from his years of experience as a professional penetration tester and educator. After reading this book, the reader will be able to create a personal penetration test lab that can deal with real-world vulnerability scenarios. "...this is a detailed and thorough examination of both the technicalities and the business of pen-testing, and an excellent starting point for anyone getting into the field." –Network Security

Mathematical Modeling for Big Data Analytics

  • 1st Edition
  • January 1, 2025
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 6 7 3 5 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 6 7 3 6 - 9
Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

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