Artificial Intelligence of Health-Enabled Spaces by Fadi Al-Turjman Paperback Bo

Publisher Description

Artificial Intelligence of Health-Enabled Spaces (AIoH) has made a number of revolutionary advances in clinical studies that we are aware of. Among these advances, intelligent and medical services are gaining a great deal of interest. Nowadays, AI-powered technologies are not only used in saving lives, but also in our daily life activities in diagnosing, controlling, and even tracking of COVID-19 patients. These AI-powered solutions are expected to communicate with cellular networks smoothly in the next-generation networks (5G/6G and beyond) for more effective/critical medical applications. This will open the door for other interesting research areas. This book focuses on the development and analysis of artificial intelligence (AI) model applications across multiple disciplines. AI-based deep learning models, fuzzy and hybrid intelligent systems, and intrinsic explainable models are also presented in this book. Some of the fields considered in this smart health-oriented book include AI applications in electrical engineering, biomedical engineering, environmental engineering, computer engineering, education, cyber security, chemistry, pharmacy, molecular biology, and tourism.This book is dedicated to addressing the major challenges in fighting diseases and psychological issues using AI. These challenges vary from cost and complexity to availability and accuracy. The aim of this book is hence to focus on both the design and implementation aspects of AI-based approaches in the proposed health-related solutions.Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via intelligent enabling technologies.

Table of Contents

About the Editor. List of Contributors. Chapter 1 IoT and Machine Learning-Based Smart Healthcare System for Monitoring Patients. Chapter 2 Detection and Diagnosis of COVID- 19 from Chest X-Ray Images Using Deep Learning. Chapter 3 Analysing the Stages of Diabetic Retinopathy Using Deep Learning Techniques. Chapter 4 COVID-19 Detection Based on Deep Learning Feature Extraction and AdaBoost Ensemble Classifier. Chapter 5 Deep Learning and Transfer Learning Models for Detection of COVID-19. Chapter 6 Industry 4.0 Challenges and Applications in the Healthcare Industry with Emerging Technologies. Chapter 7 Cyber-Security Countermeasures and Vulnerabilities to Prevent Social-Engineering Attacks. Chapter 8 Development of a COVID-19 Tracking System. Chapter 9 An Overview of Autonomous Perception for a Robotic Arm. Chapter 10 Artificial Intelligence-Based Methods for SARS-CoV-2 Detection with CRISPR Systems. INDEX.

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