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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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11-20 of 2559 results in All results

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.

Interdependent Human-Machine Teams

  • 1st Edition
  • January 1, 2025
  • William Lawless + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 2 4 6 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 2 4 7 - 7
Interdependent Human-Machine Teams: The Path to Autonomy examines the foundations, metrics, and applications of human-machine systems, the legal ramifications of autonomy, trust by the public, and trust by the users and AI systems of their users, integrating concepts from various disciplines such as AI, machine learning, social sciences, quantum mechanics, and systems engineering. In this book, world-class researchers, engineers, ethicists, and social scientists discuss what machines, humans, and systems should discuss with each other, to policymakers, and to the public.It establishes the meaning and operation of “shared contexts” between humans and machines, policy makers, and the public and explores how human-machine systems affect targeted audiences (researchers, machines, robots, users, regulators, etc.) and society, as well as future ecosystems composed of humans, machines, and systems.

Fixed Point Optimization Algorithms and Their Applications

  • 1st Edition
  • January 1, 2025
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 3 5 8 6 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 3 5 8 7 - 7
Fixed Point Optimization Algorithms and Their Applications discusses how the relationship between fixed point algorithms and optimization problems is connected and demonstrates hands-on applications of the algorithms in fields such as image restoration, signal recovery, and machine learning. The author presents algorithms for non-expansive and generalized non-expansive mappings in Hilbert space, and includes solutions to many optimization problems across a range of scientific research and real-world applications. From foundational concepts, the book proceeds to present a variety of optimization algorithms, including fixed point theories, convergence theorems, variational inequality problems, minimization problems, split feasibility problems, variational inclusion problems, and equilibrium problems.This book will equip readers with the theoretical mathematics background and necessary tools to tackle challenging optimization problems involving a range of algebraic methods, empowering them to apply these techniques in their research, professional work, or academic pursuits.

Sensor Networks for Smart Hospitals

  • 1st Edition
  • January 1, 2025
  • Tuan Anh Nguyen
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 6 3 7 0 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 6 3 7 1 - 9
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. Sensor Networks for Smart Hospitals, together with companion volume 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, Sensor Networks for Smart Hospitals is an invaluable resource for researchers and healthcare practitioners in their drive to use technology to improve the lives of patients.

Mined Individuals in Large Networks

  • 1st Edition
  • January 1, 2025
  • Christophe Prieur
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 0 4 6 8 - 5
Mined Individuals in Large Networks presents an overview of one century of research analyzing the growth of networks in our individualist societies, including methods from computer science to address questions within social sciences. The book shows how, with huge data sets, it is possible to gather vast amounts of information on individuals from large scale and social networks. At stake is the ability for the citizens to keep a grip on the social changes evolving around networking. The public concern about massive online surveillance shows the need for tools and methods that would not be only in the hands of some powerful happy few. Now that social network analysis is done by more computer scientists than social scientists, it is essential to remember the social implications of algorithms when they are applied to human beings.

Advances in Cancer Biomarkers Research

  • 1st Edition
  • December 2, 2024
  • Anand Narayan Singh + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 5 2 5 8 - 3
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 5 2 5 9 - 0
Advances in Cancer Biomarkers Research provides a thorough and detailed description of cancer biomarkers for diagnostic, prognostic and therapeutics in several cancer types. The book presents a compendium of topics related to current advanced research, along with fundamental knowledge that will help readers fully comprehend the field of cancer biomarkers. Topics discussed include such the role of genetic mechanisms, epigenetics, DNA and microRNA in different cancers, signaling pathways and exosomes. In addition, the book discusses biomarker research applied to several cancer types, such as head and neck, urological, lung, bone tumors, hematological and neurological malignancies and breast cancers.This will be a valuable resource for cancer researchers, oncologists, graduate students and members of the biomedical field who are interested in the potential of biomarkers in cancer research and treatment.

Biomedical Robots and Devices in Healthcare

  • 1st Edition
  • December 2, 2024
  • Faiz Iqbal + 3 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 3 - 2 2 2 0 6 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 2 2 0 7 - 8
Biomedical Robots and Devices in Healthcare: Opportunities and Challenges for Future Applications explores recent advances and challenges involved in using these techniques in healthcare and biomedical engineering, offering insights and guidance to researchers, professionals, and graduate students interested in this area. The book covers key topics such as the current state-of-the-art in biomedical robotics and devices, the role of emerging technologies like artificial intelligence and machine learning, rehabilitation robotics, and the optimization techniques for optimal design and control. The book concludes by exploring the potential future developments and trends in the field of biomedical robotics and devices and their healthcare implications.

High Performance Computing

  • 2nd Edition
  • December 1, 2024
  • Thomas Sterling + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 3 0 3 5 - 0
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 2 1 2 - 0
Performance Computing: Modern Systems and Practices is a fully comprehensive and easily accessible treatment of high performance computing, covering fundamental concepts and essential knowledge while also providing key skills training. With this book, students will begin their careers with an understanding of possible directions for future research and development in HPC, domain scientists will learn how to use supercomputers as a key tool in their quest for new knowledge, and practicing engineers will discover how supercomputers can employ HPC systems and methods to the design and simulation of innovative products.This new edition has been fully updated, and has been reorganized and restructured to improve accessibility for undergraduate students while also adding trending content such as machine learning and a new chapter on CUDA.

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