Sanjiban Sekhar Roy – författare

Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world.

Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text.

This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments.

Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics.

This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments.

Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics.

The book extensively explores Brain-Computer Interfaces (BCIs), emphasizing both the theoretical foundations and practical applications within this rapidly advancing field. It provides a thorough coverage of BCI fundamentals and practical implementation using MATLAB®. It begins with an introduction, covering the history of BCIs, components, and the pivotal role MATLAB® plays in their development. The book explores various aspects such as signal processing, data acquisition, rapid prototyping, machine learning, and real-time data processing, all within the MATLAB® environment. Additionally, it delves into the community and support available, along with open-source BCI toolboxes and integration with external devices. Moving forward, the book dives into the fundamentals of BCIs, including their definition, applications, principles, and components. It covers different types of brain signals utilized in BCI systems and the challenges involved in their design, such as signal reliability, userfriendliness, privacy, and regulatory issues. It discusses their principles, implementation in MATLAB®, and practical considerations for training and evaluating classification models. Finally, the book concludes with real-world case studies and practical examples, demonstrating the application of MATLAB® in BCI projects.

This book is an essential reading for researchers, engineers, students, and practitioners seeking to explore the fascinating intersection of neuroscience, signal processing, and machine learning through MATLAB-based BCI development.

The book extensively explores Brain-Computer Interfaces (BCIs), emphasizing both the theoretical foundations and practical applications within this rapidly advancing field. It provides a thorough coverage of BCI fundamentals and practical implementation using MATLAB®. It begins with an introduction, covering the history of BCIs, components, and the pivotal role MATLAB® plays in their development. The book explores various aspects such as signal processing, data acquisition, rapid prototyping, machine learning, and real-time data processing, all within the MATLAB® environment. Additionally, it delves into the community and support available, along with open-source BCI toolboxes and integration with external devices. Moving forward, the book dives into the fundamentals of BCIs, including their definition, applications, principles, and components. It covers different types of brain signals utilized in BCI systems and the challenges involved in their design, such as signal reliability, userfriendliness, privacy, and regulatory issues. It discusses their principles, implementation in MATLAB®, and practical considerations for training and evaluating classification models. Finally, the book concludes with real-world case studies and practical examples, demonstrating the application of MATLAB® in BCI projects.

This book is an essential reading for researchers, engineers, students, and practitioners seeking to explore the fascinating intersection of neuroscience, signal processing, and machine learning through MATLAB-based BCI development.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

"Generative Artificial Intelligence (AI) Approaches for Industrial Applications" explores the transformative potential of Generative AI technologies across various industries. With contributions from international scholars and experts, this book provides a comprehensive overview of the latest trends, mathematical foundations, and practical applications of Generative AI models. Key sections examine the fundamental concepts of Generative AI, including Generative Adversarial Networks (GANs) and their ethical and security considerations. Special attention is given to the revolutionary impact of Generative AI in healthcare technologies, clinical decision-making, and predictive maintenance within the manufacturing sector. Additionally, the role of Generative AI in FinTech, particularly in redefining business models and enhancing digital security, is thoroughly examined. This book features cutting-edge research on text summarization, age progression using GANs, and integrating AI with regulatory practices. This book is a vital resource for academics, professionals, and practitioners bridging the gap between theoretical AI frameworks and their real-world industrial applications, offering insights into how Generative AI is shaping the future of industries worldwide.