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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.

BooksJournals

    • Professional Penetration Testing

      • 3rd Edition
      • January 21, 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

    • Probability for Deep Learning Quantum

      • 1st Edition
      • January 21, 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.

    • Computational Intelligence for Genomics Data

      • 1st Edition
      • January 21, 2025
      • Babita Pandey + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 0 8 0 6
      • eBook
        9 7 8 0 4 4 3 3 0 0 8 1 3
      Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

    • Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT)

      • 1st Edition
      • January 20, 2025
      • Yacine Atif + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 2 2 1 7 7
      • eBook
        9 7 8 0 4 4 3 2 2 2 1 8 4
      Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms.The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies.

    • Soft Computing in Smart Manufacturing and Materials

      • 1st Edition
      • January 20, 2025
      • Sudan Jha + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 9 2 7 8
      • eBook
        9 7 8 0 4 4 3 2 9 9 2 8 5
      Soft Computing in Smart Manufacturing and Materials explains the role of soft computing in the manufacturing industries. It presents the techniques, concepts and design principles behind smart soft computing, and describes how they can be applied in the development and manufacture of smart materials. It provides perspectives for design and commissioning of intelligent applications, including in health care, agriculture, and production assembly, and reviews the latest intelligent technologies and algorithms related to the methodologies of monitoring and mitigation of sustainable engineering.

    • Agent-Based Models with MATLAB

      • 1st Edition
      • January 20, 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
      Agent-Based Models with MATLAB introduces Agent-Based Modeling (ABM), one of the most important methodologies for complex systems modeling. The book explores 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. It comes with a large amount of software code that accompanies 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.This book enables computer scientists, mathematicians, researchers, and engineers to apply ABM in a wide range of research and engineering applications. It gradually advances from basic to more advanced methods while reinforcing complex systems through practical, hands-on applications of various computational models.

    • Quantum Computing for Healthcare Data

      • 1st Edition
      • January 17, 2025
      • Gayathri Nagasubramanian + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 2 9 7 2
      • eBook
        9 7 8 0 4 4 3 2 9 2 9 8 9
      Quantum Computing for Healthcare Data: Revolutionizing the Future of Medicine presents an advanced overview of the fundamentals of quantum computing, from the transition of traditional to quantum computing, to the challenges and opportunities encountered as various industries enter into the paradigm shift. The book investigates how quantum AI, quantum data processing, and quantum data analysis can best be integrated into healthcare data systems. The book also introduces a range of case studies which feature applications of quantum computing in connected medical devices, medical simulations, robotics, medical diagnosis, and drug discovery. The book will be a valuable resource for researchers, graduate students, and professional programmers and computer engineers working in the areas of healthcare data management and analytics, blockchain, IoT, and big data analytics.

    • Sensor Networks for Smart Hospitals

      • 1st Edition
      • January 17, 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
      Sensor Networks for Smart Hospitals 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 well-being and make informed decisions about treatment. Written by experts in the field, this book is an invaluable resource for researchers and healthcare practitioners in their drive to use technology to improve the lives of patients. Data from sensor networks via the smart hospital framework is comprised of three main layers: data, insights, 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.

    • The Digital Doctor

      • 1st Edition
      • January 15, 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.

    • Neural Network Algorithms and Their Engineering Applications

      • 1st Edition
      • January 9, 2025
      • Chao Huang + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 2 0 2 6
      • eBook
        9 7 8 0 4 4 3 2 9 2 0 3 3
      Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book provides readers with a complete study of how to use artificial neural networks to design a population-based metaheuristic algorithm, which in turn promotes the application of artificial neural networks in the field of engineering optimization.The authors provide a deep discussion for the potential application of machine learning methods in improving the optimization performance of the neural network algorithm, helping readers understand how to use machine learning methods to design improved versions of the algorithm. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers' understanding of methods covered and facilitate readers' ability to apply the algorithms to their own research and development projects.

Related subjects

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