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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.

BooksJournals

    • Connected Diagnoses

      • 1st Edition
      • March 1, 2026
      • Keshav Kaushik + 1 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 8 2 9 9 4
      • eBook
        9 7 8 0 4 4 3 3 8 3 0 0 7
      Connected Diagnoses: IoT, Healthcare, and Digital Forensics investigates the complex intersection of IoT, healthcare, and digital forensics. This book explores the intricate relationships between these fields, with a focus on cybersecurity, patient data ethics, and challenges in IoT investigations. This book advances knowledge on leveraging IoT securely to enhance patient care and digital forensic analysis, providing significant insights from experts along with practical guidance for those operating at the crossroads of these critical disciplines. The book helps professionals grasp, adapt to, and capitalize on the interconnected nature of emerging technologies to ensure ethics, security, and safety. It is a comprehensive resource that benefits researchers and practitioners seeking to understand the convergence of medical technology, interconnected devices, and digital forensics.

    • Mastering Cloud Computing

      • 2nd Edition
      • March 1, 2026
      • Rajkumar Buyya + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 4 0 4 3 5 1
      • eBook
        9 7 8 0 4 4 3 4 0 4 3 6 8
      Mastering Cloud Computing: Foundations and Applications Programming, Second Edition is designed for readers learning to develop cloud computing applications. Today’s applications don’t live on a single computer but are deployed from and reside on a virtual server, accessible anywhere, any time. Today’s application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. The book covers all you need to develop applications for cloud architectures, including industrial platforms and security considerations. Real-world case studies and examples demonstrate the key concepts, with exercises and labs throughout the book.

    • AI, Blockchain and Social Network on Urban Crisis Management: Leveraging Emerging Technologies for Disaster Response and Resilience in Smart Cities

      • 1st Edition
      • Volume 142
      • April 1, 2026
      • English
      • Hardback
        9 7 8 0 4 4 3 3 1 3 6 8 4
      • eBook
        9 7 8 0 4 4 3 3 1 3 6 9 1
      AI, Blockchain and Social Network on Urban Crisis Management, Volume 142 in the Advances in Computers series, examines how cutting-edge digital technologies are reshaping disaster preparedness and response. This volume highlights the convergence of artificial intelligence, blockchain, and social network analysis to predict, prevent, and manage urban crises. Chapters explore diverse applications, from AI-driven deforestation monitoring in the Colombian Amazon and diffusion modeling for enhanced oil recovery, to blockchain-based citizen voting systems, metaverse-enabled disaster management, and graph-theoretic approaches for resource mobilization. Ethical AI for law enforcement training, social media’s role in disaster resilience, and innovative methods such as YOLO-based pothole detection further showcase how these technologies support sustainable cities and green energy initiatives. Collectively, the volume provides a multidisciplinary perspective on building smarter, safer, and more resilient urban environments.

    • 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.

    • Data Science for Teams

      • 1st Edition
      • July 30, 2025
      • Harris V. Georgiou
      • English
      • Paperback
        9 7 8 0 4 4 3 3 6 4 0 6 8
      • eBook
        9 7 8 0 4 4 3 3 6 4 0 7 5
      Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

    • 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.

    • Artificial Intelligence Innovations for Biomedical Engineering and Healthcare

      • 1st Edition
      • June 18, 2025
      • Alma Y Alanis + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 2 8 5 6 5
      • eBook
        9 7 8 0 4 4 3 3 2 8 5 7 2
      Artificial Intelligence Innovations for Biomedical Engineering and Healthcare bridges the evolving domains of artificial intelligence and biomedical engineering and healthcare. In an era where data-driven insights and precision medicine are essential in healthcare, this book explores emerging trends and showcases AI's potential in transforming patient care, diagnosis, and the treatment of chronic diseases. It simplifies the relationship between artificial intelligence and biomedical engineering, elucidating how these technologies are revolutionizing self-care. The book goes on to examine how advanced technologies, including complex networks and AI-driven diagnostics are reshaping the healthcare landscape.From decoding complex networks to revealing AI's role in treating chronic diseases, this book serves as a guide to understanding how innovation is reshaping the healthcare landscape.

    • Mathematical Modeling for Big Data Analytics

      • 1st Edition
      • November 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.

    • Tactile Robotics

      • 1st Edition
      • June 20, 2025
      • Qiang Li
      • English
      • Paperback
        9 7 8 0 4 4 3 2 1 9 1 3 9
      • eBook
        9 7 8 0 4 4 3 2 1 9 1 4 6
      Tactile Robotics structures and unifies the information processing of tactile data—not only for extracting object property but also for controller computation. This book systematically introduces tactile sensors, perception, and control, providing readers with no prior background with a better sense and knowledge of robotics and machine learning and helping users understand the concept of tactile robots and their various applications for use in real-world scenarios.

    • Intelligent Data Analytics for Solar Energy Prediction and Forecasting

      • 1st Edition
      • June 26, 2025
      • Amit Kumar Yadav + 2 more
      • English
      Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization explores the utilization of advanced neural networks, machine learning and data analytics techniques for solar radiation prediction, solar energy forecasting, installation and maximum power generation. The book addresses relevant input variable selection, solar resource assessment, tilt angle calculation, and electrical characteristics of PV modules, including detailed methods, coding, modeling and experimental analysis of PV power generation under outdoor conditions. It will be of interest to researchers, scientists and advanced students across solar energy, renewables, electrical engineering, AI, machine learning, computer science, information technology and engineers. In addition, R&D professionals and other industry personnel with an interest in applications of AI, machine learning, and data analytics within solar energy and energy systems will find this book to be a welcomed resource.

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