AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies offers a comprehensive examination of the intricate relationship between humans and machines, particularly through the transformative lens of artificial intelligence (AI). As AI technologies rapidly evolve, understanding their implications for human-machine interaction (HMI) has become essential across various domains, especially healthcare. Structured into well-defined chapters, the b...

Taking a broad, multidisciplinary approach to the ethical, legal, and practical dimensions of developing AI, the book puts key issues such as bias, transparency, accountability, and privacy at center stage. From computer science through law and ethics into policy, it lays down the roadmap on which developers, data scientists, and policymakers could bring AI technologies properly attuned to societal values. This book can be read as a resource for those who want to be able to guide the devel...

The book explores the complete system perspective, underlying theories, modelling, and the applications of pattern recognition in Healthcare Recommender System. Considering the interest of researchers and academicians, editors here aim to present this book in a multidimensional perspective that will be covering Healthcare Recommender Systems in depth, considering pattern recognition techniques using amalgamation of emerging technologies. It aims to cover all topics ranging from discussion ...

Gain a competitive edge in the semiconductor industry with this essential guide, which provides the practical insights and machine learning techniques needed to optimize the fabrication of hybrid nanodevices for integrated circuits.Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning explores the intersection of advanced manufacturing techniques and machine learning applications in the field of nanotechnology, specifically focusing on ...

This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive di...