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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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71-80 of 2576 results in All results

Decision-Making Models

  • 1st Edition
  • July 24, 2024
  • Tofigh Allahviranloo + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 6 1 4 7 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 6 1 4 8 - 3
Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems.Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty.

Distributed Optimization and Learning

  • 1st Edition
  • July 18, 2024
  • Zhongguo Li + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 6 3 6 - 7
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 6 3 7 - 4
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.

Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications

  • 1st Edition
  • July 13, 2024
  • Siddhartha Bhattacharyya + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 5 3 3 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 5 3 2 - 1
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition

  • 1st Edition
  • July 13, 2024
  • Mohammadali Ahmadi
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 4 0 1 0 - 2
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 4 0 1 1 - 9
Artificial Intelligence for a More Sustainable Oil and Gas Industry and the Energy Transition: Case Studies and Code Examples presents a package for academic researchers and industries working on water resources and carbon capture and storage. This book contains fundamental knowledge on artificial intelligence related to oil and gas sustainability and the industry’s pivot to support the energy transition and provides practical applications through case studies and coding flowcharts, addressing gaps and questions raised by academic and industrial partners, including energy engineers, geologists, and environmental scientists. This timely publication provides fundamental and extensive information on advanced AI applications geared to support sustainability and the energy transition for the oil and gas industry.

CUDA Fortran for Scientists and Engineers

  • 2nd Edition
  • July 11, 2024
  • Gregory Ruetsch + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 1 9 7 7 - 1
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 1 9 7 6 - 4
CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. In order to add CUDA Fortran to existing Fortran codes, they explain how to understand the target GPU architecture, identify computationally-intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance – all in Fortran, without having to rewrite in another language.Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison.This second edition provides much needed updates on how to efficiently program GPUs in CUDA Fortran. It can be used either as a tutorial on GPU programming in CUDA Fortran as well as a reference text.

Gesture Recognition

  • 1st Edition
  • July 9, 2024
  • Qiguang Miao + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 2 8 9 5 9 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 2 8 9 6 0 - 6
Gesture Recognition: Theory and Applications covers this important topic in computer science and language technology that has a goal of interpreting human gestures via mathematical algorithms. The book begins by examining the computer vision-based gesture recognition method, focusing on the theory and related research results of various recent gesture recognition technologies. The book takes the evolutions of gesture recognition technology as a clue, systematically introducing gesture recognition methods based on handcrafted features, convolutional neural networks, recurrent neural networks, multimodal data fusion, and visual attention mechanisms.Three gesture recognition-based HCI (Human Computer Interaction) practical cases are introduced. Finally, the book looks at emerging research trends and application.

Cancer Epigenetics and Nanomedicine

  • 1st Edition
  • June 26, 2024
  • Prashant Kesharwani + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 2 0 9 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 2 1 0 - 0
Cancer Epigenetics and Nanomedicine: Targeting the Right Player via Nanotechnology is a complete package that provides a comprehensive and thorough understanding of the key players that modulate the various steps of carcinogenesis and malignant progression of the disease and the critical targets to be exploited for developing novel modalities of diagnosis and therapeutics.Since epigenetic aberrations can be potentially reversed and restored to their normal state through epigenetic therapy, the book also discusses the challenges and the future of the field with the cutting-edge revelations and limitations that this research endeavor can offer, thereby helping the readers to enhance their critical thinking and adopt strategies of therapeutic importance.

Fractional Calculus

  • 1st Edition
  • June 21, 2024
  • Behzad Ghanbari
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 3 1 5 0 0 - 8
  • eBook
    9 7 8 - 0 - 4 4 3 - 3 1 5 0 1 - 5
Fractional Calculus: Bridging Theory with Computational and Contemporary Advances is an authoritative and comprehensive guide that delves into the world of fractional calculus, offering a unique blend of theoretical foundations, numerical algorithms, practical applications, and innovative perspectives. This book explores the mathematical framework of fractional calculus and its relevance across various disciplines, providing readers with a deep understanding of this rapidly growing field. The author presents a rigorous yet accessible approach to fractional calculus, making it suitable for mathematicians, researchers, academics, graduate students, and professionals in engineering and applied sciences. The book covers a wide range of topics, including numerical methods for fractional calculus equations, fractional differential equations, fractal dynamics, and fractional control systems. It also explores applications in areas such as physics, engineering, signal processing, and data analysis. Fractional Calculus: Bridging Theory with Computational and Contemporary Advances equips readers with the necessary tools to tackle challenging problems involving fractional calculus, empowering them to apply these techniques in their research, professional work, or academic pursuits. The book provides a comprehensive introduction to the fundamentals of fractional calculus, explaining the theoretical concepts and key definitions in a clear and accessible manner. This helps readers build a strong foundation in the subject. The book then covers a range of numerical algorithms specifically designed for fractional calculus problems, explaining the underlying principles, step-by-step implementation, and computational aspects of these algorithms. This enables readers to apply numerical techniques to solve fractional calculus problems effectively. The book also provides examples that illustrate how fractional calculus is applied to solve real-world problems, providing readers with insights into the wide-ranging applications of the subject.

Pathophysiology and Treatment of Atherosclerotic Disease in Peripheral Arteries

  • 1st Edition
  • June 12, 2024
  • Aloke Virmani Finn
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 3 5 9 3 - 4
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 3 5 9 4 - 1
Pathophysiology and Treatment of Atherosclerotic Disease in Peripheral Arteries is a thorough review of the disease written by experts studying its detection and treatment. These state-of-the-art chapters summarize emerging knowledge about this important area of medicine. The pathophysiology and treatment of peripheral artery (PAD) disease remains poorly understood even by practitioners. Often it is assumed that PAD should be treated in a similar fashion to coronary artery disease (CAD), when in fact recent data suggest a distinct pathophysiology with genetic risk having some but not complete overlap with CAD.This is a novel reference of emerging data on the factors behind its development and progression, detection, and treatment suggest an emerging paradigm for this disease.

Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing

  • 1st Edition
  • June 12, 2024
  • Rajesh Kumar Tripathy + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 1 - 6
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 4 1 4 0 - 9
Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing features recent advances in machine learning coupled with new signal processing-based methods for cardiovascular data analysis. Topics in this book include machine learning methods such as supervised learning, unsupervised learning, semi-supervised learning, and meta-learning combined with different signal processing techniques such as multivariate data analysis, time-frequency analysis, multiscale analysis, and feature extraction techniques for the detection of cardiovascular diseases, heart valve disorders, hypertension, and activity monitoring using ECG, PPG, and PCG signals.In addition, this book also includes the applications of digital signal processing (time-frequency analysis, multiscale decomposition, feature extraction, non-linear analysis, and transform domain methods), machine learning and deep learning (convolutional neural network (CNN), recurrent neural network (RNN), transformer and attention-based models, etc.) techniques for the analysis of cardiac signals. The interpretable machine learning and deep learning models combined with signal processing for cardiovascular data analysis are also covered.

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