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

51-60 of 5367 results in All results

Decision Systems

1st Edition
January 1, 2026
Pallavi Vijay Chavan
English

Decision Systems: Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks provides readers with a comprehensive understanding of the principal techniques used to build effective decision-making systems. This book covers the fundamental principles and concepts of machine learning, fuzzy logic, and artificial neural networks, and explains how these techniques can be used to build intelligent decision-making systems that can learn from data, reason, and make accurate predictions. The book also presents a wide range of applications of machine learning, fuzzy logic, and artificial neural networks in various domains, such as engineering, medicine, finance, and robotics. The book also provides practical guidance on how to design and implement effective decision-making systems using these techniques and discusses the potential challenges and limitations of machine learning, fuzzy logic, and artificial neural networks, and how to overcome them. The book provides a stepwise approach to provide readers with the knowledge and tools they need to build intelligent decision-making systems, including a robust introduction to the mathematical concepts and principles necessary to understand the concepts and applications of Decision Systems and Machine Learning algorithms. Next, the book provides readers with an in-depth explanation and demonstration of two of the major machine learning techniques – Fuzzy Logic/Fuzzy Set Theory and Artificial Neural Networks – followed by an in-depth look at more advanced topics that play essential roles in making machine learning algorithms more useful in practice, including creating full-fledged Recurrent Networks and their mathematical foundations, Associative Memories, and Deep Learning networks such as Convolutional Neural Networks, Generative Adversarial Networks, Radial Basis Function Networks, Multilayer Perceptrons, and Self-Organizing Maps. The lynchpin of the book provides readers with an understanding of how the various types of techniques can be integrated to create dynamic Decision Systems. The book wraps up with coverage of challenges and opportunities in Decision Systems along with real-world applications of Decision Systems with case studies in healthcare, finance, education, social media, and agriculture.

Harnessing Artificial Intelligence to Ensure Diverse Global Teams

1st Edition
January 1, 2026
Harish Garg + 3 more
English

"Harnessing Artificial Intelligence to Ensure Diverse Global Teams" explores new research and applications of AI which can be used to address the distinct challenges of diverse, distributed teams. Incorporating compelling case studies and strategic guidance, the book demonstrates how AI can be developed and applied within systems and programs to promote inclusion, break down barriers, and enhance collaboration in cross-cultural organizational settings. Providing case studies and examples, this book equips computer scientists and engineers with actionable strategies for integrating AI seamlessly into programs and applications designed for diverse global teams. With contributions from experts in AI and team dynamics, this book will benefit leaders and team members seeking to leverage AI for improved teamwork across geographical and cultural boundaries, providing an up-to-date resource for uniting, uplifting, and optimizing global collaborations through inclusive artificial intelligence.

Data Science for Teams

1st Edition
January 1, 2026
Harris Georgiou
English

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

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.

Mechanism Design for Robotic Systems

1st Edition
December 1, 2025
Marco Ceccarelli + 1 more
English

Mechanism Design for Robotic Systems provides theoretical fundamentals, procedures, and an outline of potential solutions for mechanism design applied to robotic systems, filling the gap from conceptual design to robot manufacturing, with both theoretical background and practical suggestions for solutions to achieve a functional robot from scratch. This book is a helpful resource for roboticists and researchers who need to design a new robot from the ground up, and to analyze existing solutions, providing tools and methods to identify optimal architecture, hardware, and design tips according to different methods.In addition, the book can be a reference for formation of robot designers as used in university teaching and professional specialization.

Quantum Technology

1st Edition
December 1, 2025
Stefan Tappertzhofen + 1 more
English

Quantum Technology introduces the readers to the fundamentals, materials, and devices for quantum technology applications. The author provides the reader with the key principles of quantum and solid-state physics, and electronic, magnetic and low-dimensional materials. Then, he introduces important advances towards realization of quantum technology such as quantum computing, quantum electronics, and cryogenics. Throughout, the author explores how understanding of materials, physics and nanotechnology can lead to practical implementation and design of devices and new technologies. Enabling technologies that underpin the design of materials, devices, and applications are also discussed. Quantum Technology is suitable for materials scientists, engineers and physicists working towards the realization of quantum technologies. It may be also of interest to computer scientists working in this area.

Photonic Integrated Circuits for Sensing Applications

1st Edition
December 1, 2025
Anu Agarwal + 2 more
English

This book introduces sensors as an important application area for integrated photonics. It provides a brief historical perspective and highlights key free-space spectroscopic sensing techniques such as FTIR, Raman, SPR, and reflectometry, and it thoroughly explores the advantages of photonic integrated circuit (PIC) sensor systems, which have the potential for low SWAP-C and high performance. Topics covered include sensor system components such as waveguides (below and above 1550 nm), ring resonators and toroids, photonic crystals, MZ interferometers, light input and output, light sources (below and above 1550 nm), and spectrometers and detectors. The book considers integrated systems that employ biofunctionalization/sorbents for applications in chem–bio sensing, and it also addresses sensor manufacturing at scale, including materials, PDK development, and sensor packaging.

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

1st Edition
November 19, 2025
Joseph Bamidele Awotunde + 3 more
English

The synergy between fog computing and the IoT facilitates the growth of smart applications and services, empowering various industries to harness the full potential of IoT technologies while ensuring faster and more efficient data processing. In essence, IoT devices collect data, and fog computing provides a mechanism for processing that data closer to the source, optimizing performance, latency, privacy, and overall system efficiency. Together, these concepts contribute to building more effective and responsive IoT ecosystems. 'Advances in Fog computing and the Internet of Things for Smart Healthcare' explores the intersection of fog computing, the IoT, and places their application in the context of smart healthcare systems. The book educates researchers and graduate students about the concepts of fog computing, IoT, and their applications in healthcare, explaining how these technologies work, their benefits, and potential challenges. The book showcases real-world use cases where fog computing and IoT have been applied in healthcare. For instance, it discusses how IoT devices are used for patient monitoring, and how these technologies contribute to personalized medicine. Addressing the challenges related to privacy, security, data management, interoperability, and regulatory compliance would be crucial. The book explores strategies and solutions for overcoming these challenges in the context of healthcare. Alongside the companion book, 'Fundamentals of Fog Computing and the Internet of Things for Smart Healthcare', this book provides readers with a comprehensive understanding of how fog computing and IoT are transforming the landscape of healthcare, enabling more efficient, personalized, and accessible healthcare services.

Modeling, Dynamics and Control approaches for Modern Robotics

1st Edition
November 3, 2025
English

The field of robotics has seen significant advances in recent years, spurred by breakthroughs in technology and computing power. Modern robotics encompasses a wide range of applications, from autonomous vehicles and industrial automation to humanoid robots and medical robotics. These advances have paved the way for more complex and sophisticated robotic systems capable of performing a variety of tasks with precision and efficiency. The purpose of this book is to explore and investigate various approaches related to modeling, dynamics and control in the context of modern robotics. The objective is to improve the understanding and development of robotic systems by applying the modern theoretical and practical principles of these fields. By studying modeling aspects, researchers aim to create accurate representations of robot kinematics and dynamics, allowing better analysis and prediction of robot behavior. Additionally, this title focuses on control approaches, aiming to design algorithms and strategies for precise and efficient control of robotic systems. This involves developing control schemes that optimize robot performance, provide stability, and allow adaptation to changing environments or tasks. The research also considers the integration of sensory feedback and perception systems, allowing robots to interact with their environment and make informed decisions.

Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

1st Edition
November 1, 2025
Tilottama Goswami + 2 more
English

As pursuit of the Sustainable Development Goals (SDGs) becomes central to our global mission, there is a growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues effectively, one should be able to measure and analyze progress. Data Driven Insights and Analytics for Measurable Sustainable Development Goals bridges the gap between qualitative and quantitative assessments, recognizing that the SDGs are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how the 5 Parts of SDGs intersect and influence each other, providing a holistic perspective on sustainable development. The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance, and thus offering valuable insights into where we stand on these critical goals. In addition, to make informed decisions and drive the SDGs forward, readers need more than just an understanding of the current situation. The book demonstrates the need for prescriptive analytics, a methodology that guides actions. Prescriptive analytics provides actionable recommendations based on data analysis, helping readers to identify specific interventions and policies that can accelerate progress. Furthermore, predictive analytics is vital in anticipating future trends and challenges. By showing how readers can forecast potential obstacles and opportunities, the book presents tools for taking proactive measures to navigate our path toward the SDGs effectively. By offering a data-driven approach, the book ensures that every action taken is based on sound analysis, making a tangible difference in building a better and safer world for all.