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

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.

Iris and Periocular Recognition using Deep Learning

1st Edition
June 12, 2024
Ajay Kumar
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 7 3 1 8 - 6
eBook
9 7 8 - 0 - 4 4 3 - 2 7 3 1 9 - 3

Iris and Periocular Recognition using Deep Learning systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many applications in sectors such as healthcare, online education, e-business, metaverse, and entertainment. This is the first-ever book devoted to iris recognition that details cutting-edge techniques using deep neural networks. This book systematically introduces such algorithmic details with attractive illustrations, examples, experimental comparisons, and security analysis. It answers many fundamental questions about the most effective iris and periocular recognition techniques.

Medical Modeling

3rd Edition
June 8, 2024
Richard Bibb + 3 more
English
Paperback
9 7 8 - 0 - 3 2 3 - 9 5 7 3 3 - 5
eBook
9 7 8 - 0 - 3 2 3 - 9 5 7 3 4 - 2

Medical Modelling: The Application of Advanced Design and Rapid Prototyping Techniques in Medicine, Third Edition provides readers with a thorough update of the core contents, along with key information on innovative imaging techniques, additive manufacturing technologies, and a range of applied case studies. This comprehensive new edition includes new coverage of advanced technologies, such as selective laser melting, electron beam melting, multi jet fusion, and more. The extensive section of peer-reviewed case studies is thoroughly updated and includes additional clinical examples, describing the practical applications of advanced design technologies in surgical, prosthetic, orthotic, dental and research applications.Finally, the book explores the future potential of medical modeling, such as in simulations for training, the development of new medical devices, and more.

Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem

1st Edition
June 7, 2024
Dominique J Monlezun
English
Paperback
9 7 8 - 0 - 4 4 3 - 2 1 5 9 7 - 1
eBook
9 7 8 - 0 - 4 4 3 - 2 1 5 9 6 - 4

Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world’s first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development.Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations—amid digitalization, deglobalization, and demographic shifts—dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public–private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity’s next era—while recovering what that humanity means, and why it is worth saving.

Cognitive Science, Computational Intelligence, and Data Analytics

1st Edition
June 6, 2024
Vikas Khare + 2 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 1 6 0 7 8 - 3
eBook
9 7 8 - 0 - 4 4 3 - 1 6 0 7 9 - 0

Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

Towards Neuromorphic Machine Intelligence

1st Edition
June 5, 2024
Hong Qu
English
Paperback
9 7 8 - 0 - 4 4 3 - 3 2 8 2 0 - 6
eBook
9 7 8 - 0 - 4 4 3 - 3 2 8 2 1 - 3

Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering. In recent years, neural networks have re-emerged in relation to AI, representing a well-grounded paradigm rooted in disciplines from physics and psychology to information science and engineering.This book represents one of the established cross-over areas where neurophysiology, cognition, and neural engineering coincide with the development of new Machine Learning and AI paradigms. There are many excellent theoretical achievements in neuron models, learning algorithms, network architecture, and so on. But these achievements are numerous and scattered, with a lack of straightforward systematic integration, making it difficult for researchers to assimilate and apply. As the third generation of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) simulate the neuron dynamics and information transmission in a biological neural system in more detail, which is a cross-product of computer science and neuroscience. The primary target audience of this book is divided into two categories: artificial intelligence researchers who know nothing about SNNs, and researchers who know a lot about SNNs. The former needs to acquire fundamental knowledge of SNNs, but the challenge is that much of the existing literature on SNNs only slightly mentions the basic knowledge of SNNs, or is too superficial, and this book gives a systematic explanation from scratch. The latter needs learning about some novel research achievements in the field of SNNs, and this book introduces the latest research results on different aspects of SNNs and provides detailed simulation processes to facilitate readers' replication. In addition, the book introduces neuromorphic hardware architecture as a further extension of the SNN system.The book starts with the birth and development of SNNs, and then introduces the main research hotspots, including spiking neuron models, learning algorithms, network architectures, and neuromorphic hardware. Therefore, the book provides readers with easy access to both the foundational concepts and recent research findings in SNNs.

Federated Learning for Digital Healthcare Systems

1st Edition
June 2, 2024
Agbotiname Lucky Imoize + 3 more
English
Paperback
9 7 8 - 0 - 4 4 3 - 1 3 8 9 7 - 3
eBook
9 7 8 - 0 - 4 4 3 - 1 3 8 9 6 - 6

Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.

Related subjects

Applications

Artificial 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