Mohendra Roy – författare

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

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

Industry 4.0 is used interchangeably with the fourth industrial revolution and represents a new stage in the organization asnd control of the industrial value chain. Cyber-physical systems form the basis of industry 4.0 (e.g., ''smart machines''). They use modern control systems, have embedded software systems, can be addressed via IoT (the Internet of Things), and may use extensive data analytics and/or articifical inteligence systems to operate autonomously.

The aim of this book is to provide detailed insights into the state of art techniques in AI, IoT, Blockchain technology and associated technologies which play a vital role in the implementation of a successful project for upcoming and practicing engineers. Owing to its multidisciplinary nature, Industry 4.0 is not a single topic but a combination of a multitude of technologies from different domains. Keeping this in mind the book includes the following topics:

Artificial intelligence Internet of things Blockchain technology Digital manufacturing Robotics Cybersecurity

The book will be a comprehensive guide to academicians and engineers who want to align with recent trends of fourth industrial revolution.

Industry 4.0 is used interchangeably with the fourth industrial revolution and represents a new stage in the organization asnd control of the industrial value chain. Cyber-physical systems form the basis of industry 4.0 (e.g., ''smart machines''). They use modern control systems, have embedded software systems, can be addressed via IoT (the Internet of Things), and may use extensive data analytics and/or articifical inteligence systems to operate autonomously.

The aim of this book is to provide detailed insights into the state of art techniques in AI, IoT, Blockchain technology and associated technologies which play a vital role in the implementation of a successful project for upcoming and practicing engineers. Owing to its multidisciplinary nature, Industry 4.0 is not a single topic but a combination of a multitude of technologies from different domains. Keeping this in mind the book includes the following topics:

Artificial intelligence Internet of things Blockchain technology Digital manufacturing Robotics Cybersecurity

The book will be a comprehensive guide to academicians and engineers who want to align with recent trends of fourth industrial revolution.

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

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