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

Digital Transformation and Equitable Global Health

1st Edition
September 10, 2025
Arletty Pinel + 2 more
English

Digital Transformation and Equitable Global Health: A Future-Ready Perspective presents a collective body of knowledge and global experiences that demonstrate current status and future trends in the use of exponential technologies and their potential for poverty reduction, improving health outcomes, strengthening health systems, and transforming traditional development aid structures. The book uses a translational innovation perspective to guide the reader—regardless of their area of expertise—on the rationale behind the co-creation of human-centered, affordable, and sustainable digital solutions.It addresses the interest of professionals from multiple areas (e.g., technology, health, social development, global financing), and it is a valuable resource for professionals, social scientists, practitioners, researchers, instructors, and undergraduate and graduate students interested in understanding the challenges and complexities of global public health and the applied uses of health technologies for equitable access to primary health care and universal health coverage.

A History of Angiogenesis

1st Edition
September 1, 2025
Gianfranco Natale + 2 more
English
Paperback
9 7 8 0 4 4 3 1 3 9 7 9 6
eBook
9 7 8 0 4 4 3 1 3 9 8 0 2

A History of Angiogenesis: Discoveries, Research and the Therapeutic Potential presents a recent history of discoveries in the angiogenic field that contribute to the development of the anatomical knowledge of the process, experimental models, and new pharmacological approaches in different angiogenesis-related diseases. This multifaceted book is enriched with deep scientific contents, and each chapter clearly defines historical steps, the emerging role during the years of new morphological findings, molecular pathways, and targets. Focus is also directed on key challenges associated and future directions.The history of angiogenesis research is an intriguing tool for discovering answers to a wide array of queries arising in the biological and medical world. This comprehensive resource skillfully integrates all the key aspects related to angiogenesis, and will be of immense help to students, researchers, and industry experts who want to explore the last two decades of angiogenesis discoveries and research.

Artificial Intelligence Data and Model Security

1st Edition
September 1, 2025
Yu-Gang Jiang + 2 more
English

Artificial Intelligence Data and Model Security: Risks, Attacks and Defenses begins with a brief review of the history of AI and AI security and then introduces the fundamental aspects of machine learning and AI security. Two key aspects are covered: data security and modelling. It provides detailed explanations of a wide range of attacks and defense algorithms related to data security, as well as adversarial attack/defense, backdoor attack/defense, and extraction attack/defense algorithms related to model security. By providing a systematic, comprehensive, and in-depth introduction to the topic, this book help readers understand the advanced attack and defense techniques in the field of AI security.

Federated Learning in Metaverse Healthcare

1st Edition
August 4, 2025
Jyotir Moy Chatterjee + 1 more
English

Federated Learning in Metaverse Healthcare: Personalized Medicine and Wellness explores the integration of the metaverse with healthcare, offering immersive experiences and personalized care. It introduces federated learning, highlighting its advantages over centralized machine learning in healthcare. The historical context and technological advancements that have led to the emergence of metaverse healthcare are explored, along with privacy-preserving methods crucial for protecting sensitive healthcare data in federated learning environments. The transformation of healthcare delivery through virtual environments, remote consultations, and immersive experiences is discussed, as well as the role of telemedicine in facilitating remote diagnostics and consultations through virtual platforms. The applications of augmented reality wearables in real-time health monitoring and wellness tracking are explored. The architecture and components of federated learning systems within metaverse healthcare environments are detailed, emphasizing the importance of secure communication protocols in safeguarding healthcare data. Federated learning's ability to deliver personalized treatment plans tailored to individual patient needs, as well as its role in predictive modeling for disease risks and prevention strategies, is examined. Virtual health coaches leveraging federated learning algorithms to provide personalized guidance and support for wellness management are also discussed. The challenges and ethical dilemmas inherent in metaverse healthcare and federated learning are considered, along with potential solutions. Finally, the future of metaverse healthcare and federated learning is speculated, highlighting emerging trends and areas for further research and development.

Edge Intelligence in Cyber-Physical Systems

1st Edition
August 1, 2025
Wei Yu
English

Edge Intelligence in Cyber-Physical Systems: Foundations and Applications provides a comprehensive overview of best practices for building edge intelligence into cyber-physical systems. This book covers the foundations and applications of synergizing machine learning at the edge of CPS, leveraging an edge computing infrastructure. Divided into four parts, the first section of the book reviews the foundations, principles, and representative application domains of CPS. The second part covers machine learning, edge computing, and their needs in CPS, defining edge intelligence and its principles, challenges, and research directions. The third part presents tutorials and foundational research works on realizing edge intelligence in representative CPS. The fourth part explores the problem space of threats and countermeasures in building edge intelligence into CPS. Researchers, graduate students and professionals in computer science, data science, and electrical engineering will find this to be a valuable resource on the principles and applications of edge intelligence in cyber-physical systems as well as the development of interdisciplinary techniques to advance the field.

Mathematical Modeling for Big Data Analytics

1st Edition
August 1, 2025
Passent El-Kafrawy + 1 more
English
Paperback
9 7 8 0 4 4 3 2 6 7 3 5 2
eBook
9 7 8 0 4 4 3 2 6 7 3 6 9

Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

Advanced Intelligence Methods for Data Science and Optimization

1st Edition
August 1, 2025
Amir Hossein Gandomi + 2 more
English

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains.It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

Intelligent and Soft Computing

1st Edition
August 1, 2025
Ishaani Priyadarshini + 3 more
English
Paperback
9 7 8 0 4 4 3 3 0 0 9 0 5
eBook
9 7 8 0 4 4 3 3 0 0 9 1 2

Intelligent and Soft Computing: Techniques and Applications illustrates the synergistic effect of cutting-edge developments in all areas of both fields to build intelligent software systems to solve a multitude of problems. The book offers an introduction to artificial intelligence as well as fuzzy logic concepts, systems, and methods. Additional sections focus on AI tools and applications, search and data mining, reasoning and evolution, as well as nature-inspired computing techniques. Applications and case studies are included in all chapters, making this an ideal resource for researchers, graduate students, and professionals working in these domains.Readers will find this to be a timely reference on current and future developments in this emerging domain for decision support, management, information technology, and business applications.

Decentralized Optimization in Networks

1st Edition
August 1, 2025
Qingguo Lu + 5 more
English

Decentralized algorithms are useful for solving large-scale complex optimization problems, which not only alleviate the single-point resource bottleneck problem of centralized algorithms, but also possess higher scalability. Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and problem-solving approaches to decentralized optimization. It teaches how to apply decentralized optimization algorithms to improve optimization efficiency (communication efficiency, computational efficiency, fast convergence), solve large-scale problems (training for large-scale datasets), achieve privacy preservation (effectively counter external eavesdropping attacks, differential attacks, etc), and overcome a range of challenges in complex decentralized network environments (random sleep, random link failures, time-varying, directed, etc). It focuses on: 1) communication-efficiency: event-triggered communication, random link failures, zeroth-order gradients. 2) computation-efficiency: variance-reduction, Polyak’s projection, stochastic gradient, random sleep. 3) privacy preservation: differential privacy, edge-based correlated perturbations, conditional noises. It uses simulation results, including practical application examples, to illustrate the effectiveness and the practicability of decentralized optimization algorithms.

Mastering Prompt Engineering

1st Edition
August 1, 2025
Anand Nayyar + 2 more
English
Paperback
9 7 8 0 4 4 3 3 3 9 0 4 2
eBook
9 7 8 0 4 4 3 3 3 9 0 5 9

Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs) is a comprehensive guide that takes readers on a journey through the world of Large Language Models (LLMs) and prompt engineering. Covering foundational concepts, advanced techniques, ethical considerations, and real-world case studies, this book equips both novices and experts to navigate the complex LLM landscape. It provides insights into LLM architecture, training, and prompt engineering methods, while addressing ethical concerns such as bias and privacy. Real-world case studies showcase the practical application of prompt engineering in a wide range of settings. This resource is not just for specialists but is a practical and ethically conscious guide for AI practitioners, students, scientific researchers, and anyone interested in harnessing the potential of LLMs in natural language processing and generation. Mastering Prompt Engineering serves as a gateway to a deeper understanding of LLMs and their responsible and effective utilization through its comprehensive, ethical, and practical approach.

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