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

Autonomous Vehicle Safety Solutions

1st Edition
January 1, 2026
Aparna Kumari
English
Paperback
9 7 8 0 4 4 3 3 3 5 2 8 0
eBook
9 7 8 0 4 4 3 3 3 5 2 9 7

Autonomous Vehicle Safety Solutions: Foundations, Technologies, and Prospects for the Future addresses the dynamically evolving state of the art that traverses and brings together automotive engineering, electronics engineering, and computer science. Its technical overview of smart vehicles' capabilities, propelled by headway in artificial intelligence and sensor technologies, enables in-depth understanding of the safety-assurance complexities that guide their design and operation.The comprehensive volume distinguishes itself for its treatment of several exciting, emerging opportunities and trends, including machine learning algorithms, V2X connectivity, and cybersecurity. Up-to-date applications are featured through practical examples and expert insights, bringing additional value to a discourse which underscores the critical need for ongoing safety advancements and adaptability in the fast-moving context of autonomous driving systems, with their related implications on sustainability and society as a whole.The outcome is a cornerstone resource for academia and industry alike that encourages further transformative, interdisciplinary investigations to bring safety solutions to maturity and subsequent rigorous testing for validation standards to be defined, thus realizing, in a future not distant from now, a scenario where self-driving vehicles can co-exist seamlessly with traditional modes of transportation and also integrate reliably, efficiently, and without risks within the larger infrastructure.

Challenges and Applications of Generative Large Language Models

1st Edition
January 1, 2026
Anitha S. Pillai + 2 more
English
Paperback
9 7 8 0 4 4 3 3 3 5 9 2 1
eBook
9 7 8 0 4 4 3 3 3 5 9 3 8

Large Language Models (LLMs) are a form of generative AI, based on Deep Learning, that rely on very large textual datasets, and are composed of hundreds of millions (or even billions) of parameters. LLMs can be trained and then refined to perform several NLP tasks like generation of text, summarization, translation, prediction, and more. Challenges and Applications of Generative Large Language Models assists readers in understanding LLMs, their applications in various sectors, challenges that need to be encountered while developing them, open issues, and ethical concerns. LLMs are just one approach in the huge set of methodologies provided by AI. The book, describing strengths and weaknesses of such models, enables researchers and software developers to decide whether an LLM is the right choice for the problem they are trying to solve. AI is the new buzzword, in particular Generative AI for human language (LLMs). As such, an overwhelming amount of hype is obfuscating and giving a distorted view about AI in general, and LLMs in particular. Thus, trying to provide an objective description of LLMs is useful to any person (researcher, professional, student) who is starting to work with human language. The risk, otherwise, is to forget the whole set of methodologies developed by AI in the last decades, sticking with only one model which, although very powerful, has known weaknesses and risks. Given the high level of hype around such models, Challenges and Applications of Generative Large Language Models (LLMs) enables readers to clarify and understand their scope and limitations.

Data Science for Teams

1st Edition
January 1, 2026
Harris V. Georgiou
English
Paperback
9 7 8 0 4 4 3 3 6 4 0 6 8

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.

Edge Intelligence

1st Edition
January 1, 2026
Jawad Ahmad + 5 more
English
Paperback
9 7 8 0 4 4 3 3 8 2 9 7 0
eBook
9 7 8 0 4 4 3 3 8 2 9 8 7

Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.

Consensus

1st Edition
January 1, 2026
Ali Ahmadian + 3 more
English
Paperback
9 7 8 0 4 4 3 4 0 4 3 9 9
eBook
9 7 8 0 4 4 3 4 0 4 4 0 5

Consensus: Fueling Blockchain Innovation and DApp Expansion explores the complexities of consensus mechanisms in order to shed light on emerging trends, best practices, and real-world applications that can fuel blockchain innovation while encouraging the dissemination of DApps across various industries. Additionally, the book bridges a crucial gap in the literature by providing in-depth insights into the role of consensus mechanisms in shaping the future of blockchain technology and decentralized applications. This book delves into the fundamentals of blockchain technology along with the roles and significance of vital consensus mechanisms, their underlying principles, formal specifications, functional characteristics, architecture, frameworks, and potential across diverse blockchain applications. Moreover, the book meticulously explores classification, performance metrics, and design parameters. It offers a comprehensive comparative analysis of these mechanisms, shedding light on their computational and communication complexity, strengths, weaknesses, and suitability. Additionally, the book delves into future research directions, highlighting emerging trends and areas requiring further investigation. It also addresses the efforts underway to address existing challenges and open issues within the realm of consensus mechanisms, ensuring a comprehensive understanding of the state-of-the-art in this pivotal aspect of blockchain technology. Due to the wide range of availability and evolving new consensus mechanisms, selecting an optimal and suitable consensus for a specific blockchain application is one of the crucial challenges in the development and innovation of blockchain systems. This book has also a discussion on appropriate selection algorithms based on multi-attribute decision-making for specific blockchain systems and DApps development.

Fundamentals of Statistics for Researchers and Data Analysts

1st Edition
January 1, 2026
Shashi Chiplonkar + 1 more
English
Paperback
9 7 8 0 4 4 3 4 4 7 5 1 8
eBook
9 7 8 0 4 4 3 4 4 7 5 2 5

Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

Digital Transformation and Equitable Global Health

1st Edition
December 1, 2025
Arletty Pinel + 2 more
English
Paperback
9 7 8 0 4 4 3 2 1 4 9 8 1
eBook
9 7 8 0 4 4 3 2 1 4 8 8 2

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.

Artificial Intelligence for the Water-Energy-Food Nexus

1st Edition
December 1, 2025
Shahryar Jafarinejad + 1 more
English
Paperback
9 7 8 0 4 4 3 3 4 0 1 9 2
eBook
9 7 8 0 4 4 3 3 4 0 2 0 8

Artificial Intelligence for the Water–Energy–Food Nexus considers the interrelationships among water, energy, and food, which are key to sustainable development and the potential of artificial intelligence (AI) as a useful tool to facilitate research and development within and between these areas as well as the water–energy–food (WEF) nexus.The first chapter introduces the WEF nexus concepts, assessment/analysis methodologies and tools, challenges, trends, and future perspectives. The second chapter summarizes AI applications to the water sector/industry with a focus on the importance, general applications, and real-world applications, as well as the challenges and future perspectives. The third chapter focuses on the application of AI techniques to the water and wastewater treatment systems, including treatment processes, urban drinking water systems, and integrated urban drainage systems. The fourth chapter explores the application of AI techniques to the nonrenewable and renewable energy systems, load monitoring, load demand forecasting, smart grids, energy optimization and process control, energy storage systems, and case studies and real-world applications, as well as the challenges in AI application to the energy industry and future directions. The application of AI techniques in the food industry in the areas of organoleptic properties, nutrition, toxicology, and food chemistry; biotechnology processes and microbiology security; food processing and manufacturing; conservation and storage conditions; supply chains, markets, and distribution; and food industry environments (circular economy, water, and energy management), as well as the challenges and opportunities are discussed in the fifth chapter. The final chapter reviews/presents the application of AI techniques to address problems associated with the WEF nexus. This book can be of interest to all those who are looking to understand and utilize AI techniques in the context of water, energy, food, and the WEF nexus, including researchers, advanced students, faculty, engineers, R&D, industry professionals, and policymakers.

AI and Data Science in Healthcare 5.0

1st Edition
December 1, 2025
Olfa Boubaker
English
Paperback
9 7 8 0 4 4 3 3 6 5 5 6 0
eBook
9 7 8 0 4 4 3 3 6 5 5 7 7

AI and Data Science in Healthcare 5.0 delves into the innovative developments in Healthcare 5.0, focusing on smart medical robots, devices, and connected hospitals. It explores the role of robotics in modern healthcare, including advanced wearables enhanced by deep learning. The volume also addresses the integration of blockchain technology, IoT, and cloud computing in healthcare, emphasizing real-time applications in precision healthcare. The discussion extends to energy optimization in smart hospitals, federated learning for IoMT networks, and advanced patient monitoring systems.The volume concludes with an overview of the future opportunities and challenges in Healthcare 5.0.

Advances in Fog Computing and the Internet of Things for Smart Healthcare

1st Edition
November 19, 2025
Joseph Bamidele Awotunde + 3 more
English
Paperback
9 7 8 0 4 4 3 3 3 4 4 1 2
eBook
9 7 8 0 4 4 3 3 3 4 4 2 9

Advances in Fog Computing and the Internet of Things for Smart Healthcare delves into the transformative impact of fog computing and IoT on modern healthcare systems. This comprehensive guide educates researchers and graduate students on the fundamental concepts of these technologies, illustrating their practical applications in healthcare. Real-world use cases showcase the technologies' ability to enhance patient monitoring and personalized medicine. The book also addresses significant challenges such as privacy, security, data management, and regulatory compliance, providing strategies for overcoming them.Alongside its companion book, Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare, this volume empowers various industries to leverage IoT technologies while optimizing performance and system efficiency. It highlights the crucial role of fog computing in processing data closer to the source, ensuring faster and more efficient data processing and contributing to the creation of effective IoT ecosystems. This synergy between fog computing and IoT facilitates smarter applications and services, advancing the landscape of healthcare with more efficient, personalized, and accessible services.

Related subjects

Applications

Artificial intelligence

Business intelligence

Computer systems organization

Computer vision and pattern recognition

Computing milieux

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