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

Advances in Computational Methods and Modeling for Science and Engineering

1st Edition
February 4, 2025
Hari M Srivastava + 2 more
English
Paperback
9 7 8 0 4 4 3 3 0 0 1 2 7
eBook
9 7 8 0 4 4 3 3 0 0 1 3 4

Advances in Computational Methods and Modelling in Science and Engineering explores the application of computational techniques and modeling approaches in science and engineering, providing practical knowledge and skills for tackling complex problems using numerical simulations and data analysis. This book addresses the need for a cohesive and up-to-date resource in the rapidly evolving field of computational methods. It consolidates diverse topics, serving as a one-stop guide for individuals seeking a comprehensive understanding of the subject matter. Sections focus on mathematical techniques that provide global solutions for models arising in engineering and scientific research applications by considering their long-term benefits.The mathematical treatment of these models is very helpful in understanding these models and their real-world applications. The methods and modeling techniques presented are useful for mathematicians, engineers, scientists, and researchers working on the mathematical treatment of models in a wide range of applications, including disciplines such as engineering, physics, chemistry, computer science, and applied mathematics.

Dimensionality Reduction in Machine Learning

1st Edition
February 4, 2025
Jamal Amani Rad + 2 more
English
Paperback
9 7 8 0 4 4 3 3 2 8 1 8 3
eBook
9 7 8 0 4 4 3 3 2 8 1 9 0

Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.

The Core Network for 5G Advanced

2nd Edition
February 3, 2025
Stefan Rommer + 5 more
English
Paperback
9 7 8 0 4 4 3 2 9 1 8 8 3
eBook
9 7 8 0 4 4 3 2 9 1 8 9 0

The Core Network for 5G Advanced, Second Edition covers up to the 3GPP release 17 & 18 which includes the core network for 5G Advanced as well as a large number of new features added by 3GPP to expand the initial 5G Core specifications in rel-15/16. This new release includes A complete update to reflect developments in Releases 17 and 18, along with new chapters on Service Exposure, non-3GPP access, 3GPP access for new use cases, Edge computing, Industry and Enterprise features, Regulatory services and Network automation and AI/ML, an overview of the 5G Core Architecture, and much more.Examples of 5G network deployment options for different use cases are also included. Written by authors who are heavily involved in the development of 5G standards, and who have written several successful books on 4G and 5G Core Networks, this book provides an authoritative reference on the technologies and standards of the 3GPP 5G Core network.

Blockchain and Digital Twin for Smart Hospitals

1st Edition
February 1, 2025
Tuan Anh Nguyen
English
Paperback
9 7 8 0 4 4 3 3 4 2 2 6 4
eBook
9 7 8 0 4 4 3 3 4 2 2 7 1

Blockchain and Digital Twins for Smart Healthcare describes the role of blockchain and digital twins in smart healthcare, covering 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. Medical data is collected real-time from devices and systems in 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.

Fractional Modeling of Fluid Flow and Transport Phenomena

1st Edition
January 31, 2025
Mohamed F. El-Amin
English
Paperback
9 7 8 0 4 4 3 2 6 5 0 8 2
eBook
9 7 8 0 4 4 3 2 6 5 0 9 9

Fractional Modeling of Fluid Flow and Transport Phenomena focuses on mathematical and numerical aspects of fractional-order modeling in fluid flow and transport phenomena. The book covers fundamental concepts, advancements, and practical applications, including modeling developments, numerical solutions, and convergence analysis for both time and space fractional order models. Various types of flows are explored, such as single- and multi-phase flows in porous media, involving different fluid types like Newtonian, non-Newtonian, nanofluids, and ferrofluids. This book serves as a comprehensive reference on fractional-order modeling of fluid flow and transport phenomena, offering a single resource that is currently unavailable.Fraction... modeling has gained traction in engineering and science, particularly in fluid dynamics and transport phenomena. However, its mathematical and numerical advancements have progressed relatively slowly compared to other aspects. Therefore, this book emphasizes the fractional-order modeling of fluid flow and transport phenomena to bridge this gap. Each chapter in the book delves into a specific topic closely related to the others, ensuring a cohesive and self-contained structure.

Advanced Sensors for Smart Healthcare

1st Edition
January 27, 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

Advanced Sensors for Smart Healthcare provides an invaluable resource for researchers and healthcare practitioners who are eager to use technology to improve the lives of patients. Sections highlight data from sensor networks via the smart hospital framework, including data, insights, and access. This book shows how the use of sensors to gather data on a patient's condition and the environment their care takes place in can allow healthcare professionals to monitor well-being and make informed decisions about treatment.

Applied Graph Data Science

1st Edition
January 27, 2025
Pethuru Raj + 4 more
English
Paperback
9 7 8 0 4 4 3 2 9 6 5 4 3
eBook
9 7 8 0 4 4 3 2 9 6 5 5 0

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

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.

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

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.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

Data

Graphics cad hci

Hardware and architecture

Information systems

Internet and web technology

Software

Theory and mathematics

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health