Fnirs In Neuroscience And Its Emerging Applications

FNIRS in Neuroscience and its Emerging Applications

Biometrics and Neuroscience Research in Business and Management

This book is designed to impart and convey knowledge as well as understanding of the range of biometric technologies and cybertechnologies, while also stressing their advantages and disadvantages. The authors develop an appreciation for and breadth of knowledge that spans the full range of neuroscience sub-disciplines, including behavioural, cognitive, and computational neuroscience. Furthermore, the content of the book is geared to be able to place neuroscience into an ethical context, especially showing how studying the brain and behaviour can contribute to the resolution of ethical, social, and environmental issues.

Explainable AI in Healthcare

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering. This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care