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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 2575 results in All results

Python Fast Track

  • 1st Edition
  • March 1, 2025
  • Sanjiban Sekhar Roy + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 3 8 2 3 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 3 8 2 4 - 3
Python Fast Track: A Complete Guide to Rapidly Mastering and Applying Python Programming adopts a simplified writing style and provides clear explanations to ensure ease of understanding, making it an ideal resource for those new to Python. Starting with the basics, the book covers fundamental concepts such as variables, data types, printing and prompting, lists, dictionaries, tuples, control structure, functions, and object-oriented concepts. The book includes everything you need to understand and apply more advanced programming techniques such as file handling, exception handling, and regex.This great resource is created especially for those who want to apply Python for their research and professional work in scientific computing, data analysis and machine learning, including chapters on NumPy and Pandas, two of the most popular Python application libraries. It demonstrates how to effectively master key applications of Python such as web development, software creation, task automation, and data analysis. The book covers data analysis and machine learning tasks that greatly benefit from Python, thanks to libraries such as TensorFlow and Keras that enable efficient coding.

Fractional Modelling of Fluid Flow and Transport Phenomena

  • 1st Edition
  • February 3, 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 Modelling 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.Fractional-order 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.

Soft Computing in Smart Manufacturing and Materials

  • 1st Edition
  • February 3, 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.

Blockchain and Digital Twin for Smart Hospitals

  • 1st Edition
  • February 3, 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
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 (IOMT). 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 twins and blockchain technologies. Blockchain and Digital Twins for Smart Hospitals describes the role of blockchain and digital twins in smart healthcare. It describes the ecosystem of the IOMT, 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. Together with its companion volume, 'Blockchain and Digital Twins for Smart Healthcare', the book offers insight to aid faster, smarter decisions with more efficiency to improve care for the patient.

The Core Network for 5G Advanced

  • 2nd Edition
  • February 1, 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.

Machine Learning

  • 3rd Edition
  • February 1, 2025
  • Sergios Theodoridis
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 9 2 3 8 - 5
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 9 2 3 9 - 2
Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models, Third Edition starts with the basics, including least squares regression and maximum likelihood methods, Bayesian decision theory, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines. Bayesian learning is treated in detail with emphasis on the EM algorithm and its approximate variational versions with a focus on mixture modelling, regression and classification. Nonparametric Bayesian learning, including Gaussian, Chinese restaurant, and Indian buffet processes are also presented. Monte Carlo methods, particle filtering, probabilistic graphical models with emphasis on Bayesian networks and hidden Markov models are treated in detail. Dimensionality reduction and latent variables modelling are considered in depth. Neural networks and deep learning are thoroughly presented, starting from the perceptron rule and multilayer perceptrons and moving on to convolutional and recurrent neural networks, adversarial learning, capsule networks, deep belief networks, GANs, and VAEs. The book also covers the fundamentals on statistical parameter estimation and optimization algorithms.Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all methods and techniques are explained in depth, supported by examples and problems, providing an invaluable resource to the student and researcher for understanding and applying machine learning concepts.New to this editionThe new material includes an extended coverage of attention transformers, large language models, self-supervised learning and diffusion models.

Quantum Computing for Healthcare Data

  • 1st Edition
  • February 1, 2025
  • 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.

Applied Graph Data Science

  • 1st Edition
  • February 1, 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.

Advances in Computational Methods and Modeling for Science and Engineering

  • 1st Edition
  • February 1, 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.

Computational Intelligence for Genomics Data

  • 1st Edition
  • February 1, 2025
  • 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.

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