Applications of Artificial Intelligence in the Healthcare Sector [Team-IRA]

This book deals with a different research area of cognitive IoT and explains how machine learning algorithms can be applied for cognitive IoT. It deals with applications of cognitive IoT in this pandemic (COVID-19), applications for student performance evaluation, applications for human healthcare for chronic disease prediction, use of wearable sensors and review regarding their energy optimization and how cognitive IoT helps in farming through rainfall prediction and prediction of lake levels. Features: Describes how cognitive IoT is helpful for chronic disease prediction and processing of data gathered from healthcare devices Explains different sensors available for health monitoring Explores application of cognitive IoT in COVID-19 analysis Discusses pertinent and efficient farming applications for sustaining agricultural growth Reviews smart educational aspects such as student response, performance, and behavior and instructor response, performance, and behavior This book aims at researchers, professionals and graduate students in Computer Science and Engineering, Computer Applications and Electronics Engineering, and Wireless Communications and Networking. Contents List of Figures List of Tables Preface Acknowledgments Chapter 1 Artificial Intelligence: Healthcare’s Future, Not a Mere Technology Abstract 1.1. Introduction 1.1.1. History – The Rise of AI 1.1.2. Conception of Artificial Intelligence and Its Sub-Fields 1.1.3. AI vs ML vs DL – A Comparative Outlook 1.1.4. Applications of AI in the Healthcare Domain 1.1.5. AI Systems in the Healthcare Sphere 1.1.6. AI in Health Economics and Outcomes Research 1.1.7. AI in Real-World Evidence and Real-World Studies 1.1.8. AI: Savior in COVID-19 Pandemic 1.2. Literature Survey 1.2.1. Recent Developments Where AI Acted as a Bliss 1.2.2. Challenges Faced 1.2.3. Future of AI 1.2.4. Human Intelligence vs Artificial Intelligence – Is the Debate Worth It? Conclusion References Chapter 2 Applications of Artificial Intelligence in Rural Areas Abstract 2.1. Introduction 2.1.1. Rural Development 2.1.2. What Is the Role of AI? 2.1.3. Artificial Intelligence within the Healthcare System 2.1.4. The Improvement of Rural Areas by AI 2.1.5. Drone Operators 2.1.6. Precision Animal Husbandry and Agriculture 2.1.7. Executives in Micro-Seeding Projects 2.1.8. Farm Planning with Data Visualizers 2.2. Literature Survey 2.2.1. Artificial Intelligence for Rural Development 2.2.2. Artificial Intelligence in Agriculture 2.2.3. Artificial Intelligence in Education 2.2.4. Artificial Intelligence in Healthcare 2.2.5. Application of Artificial Intelligence Technology in Agriculture Conclusion References Chapter 3 An Artificial Intelligence-Based Pharmacy in Rural Areas Abstract 3.1. Introduction 3.2. Literature Survey 3.3. Problem Statement 3.4. Challenges for Rural Health System: An Overview 3.4.1. Underutilization of Existing Rural Hospitals 3.4.2. Inadequate Human Resources 3.4.3. Lack of Community Participation 3.4.4. Remedies in Rural Health System 3.4.5. National Rural Health Mission (NRHM) 3.4.6. Janani Suraksha Yojana (JSY) 3.4.7. Mobile-Based Primary Health Care System 3.5. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries 3.6. The Prospect of Medical AI Technology 3.6.1. AI in Personalized Medicine 3.6.2. AI in Healthcare System Management 3.6.3. Medical Robots with AI 3.6.4. AI in Pharma Industry Conclusion References Chapter 4 FW-MCDM: Feature Weighted Multi-Criteria Decision-Making Techniques for Multi-Label Feature Selections Abstract 4.1. Introduction 4.2. Literature Survey 4.2.1. Multi-Label Learning 4.2.2. Background Study 4.3. Methodology 4.3.1. Feature Weighting (KNN) 4.4. Result and Discussion 4.4.1. Multi-Label Learning Evaluation Parameters 4.4.2. Experiment Conclusion References Chapter 5 Lung Cancer and Pneumonia Detection Using Image Processing and Machine Learning Abstract 5.1. Introduction 5.1.1. Pneumonia Diagnosis 5.1.2. Pneumonia Complications 5.1.3. Types of Pneumonia 5.2. Literature Survey 5.2.1. Image Processing-Based Approach 5.2.2. Machine Learning and Deep Learning-Based Approach 5.2.3. Pneumonia Detection Using X-Ray Image 5.2.3.1. Processing Using CNN 5.3. Methodology 5.3.1. RGB to Grayscale 5.3.2. Average Method 5.3.3. The Weighted Method 5.3.4. Image Segmentation 5.3.5. Feature Extraction 5.3.6. SVM (Support Vector Machine) for Classification Conclusion References Chapter 6 Deep Learning Algorithms in Healthcare Abstract 6.1. Introduction 6.2. Literature Survey 6.3. Healthcare Application Using Deep Learning Method 6.4. Challenges of Deep Learning 6.4.1 AutoML-Zero 6.4.2. Neural Architecture Search 6.4.3. Evolutionary Deep Learning 6.4.4. Data Management Challenges 6.4.5. Quantity of Input and Identification of Noncontributing Attributes 6.4.6. Activation Functions 6.4.7. Kinds of Network 6.4.8. Epochs Conclusion References Chapter 7 Applying Topic Models for Finding N-Gram Entities in Biomedical Literature Abstract 7.1. Introduction 7.2. Literature Survey 7.3. Modeling Topic N-Grams (TNG) 7.3.1. Algorithm 1: Topic N-Grams Identification 7.4. Result Conclusion References Chapter 8 A Chronic Disease Diagnosis Model for Smart Healthcare Systems Enabled by Artificial Intelligence and the Internet of Things Abstract 8.1. Introduction 8.2. Literature Survey 8.2.1. Machine Learning: Neural Networks and Deep Learning 8.2.2. Natural Language Processing 8.2.3. Rule-Based Expert Systems 8.2.4. Physical Robots 8.3. A Comparison of IoMT Monitoring Solutions 8.3.1. Physiological Parameter Analysis 8.3.2. Systems of Rehabilitation 8.3.3. Skin Pathologies and Nutritional Evaluation 8.3.4. Disease Control and Location-aware Solutions for Epidemics 8.4. Taxonomy of Smart Health 8.4.1. IoT Healthcare Services 8.4.2. Healthcare Applications 8.4.3. Smart Healthcare Requirements 8.4.4. Characteristics of Smart Healthcare Conclusion References Chapter 9 IoT-Based E-Health Monitoring System for Pre-Schoolers Abstract 9.1. Introduction 9.2. Literature Survey 9.3. System Architecture and Components Wi-Fi Chip (ESP8266) Pulse Oximeter Sensor DHT11 Sensor Battery Resistor Voltage Regulator Electrolytic Capacitor 9.4. Working Principle 9.5. Implementation 9.6. Application Area Conclusion References Chapter 10 Internet of Robotics Things (IoRT) in Healthcare Systems Abstract 10.1. Introduction 10.1.1. Gather Information Using Sensors 10.1.2. Transfer Data Using the Internet (Connectivity) 10.1.3. Collect Information and Process 10.2. Literature Survey 10.3. IoRT (Internet of Robotics Things) Devices in the Healthcare 10.3.1. Implantable Glucose Monitoring Systems 10.3.2. Activity Trackers during Cancer Treatment 10.3.3. IoT-Based Heart Disease Monitoring System 10.3.4. Medical Alert Systems 10.3.5. Ingestible Sensors 10.3.6. Traceable Inhalers 10.3.7. Wearables to Fight Depressions 10.3.8. Robots in Healthcare 10.4. Challenges Conclusion References Index About the Editors Blank Page Blank Page "This book was constructed with the syllabus of many countries' universities in mind, so that undergraduate students, postgraduate students, and university researchers can utilize it for their studies. Chapter 1 of the book mainly focuses on the background of Artificial Intelligence and its applications in various fields. Chapter 2 presents the applications of Artificial Intelligence to save lives in rural areas. In Chapter 3, applications of Artificial Intelligence in pharmacies are explored. Chapter 4 is about the use of machine learning algorithms to extract and optimize features from the imaging of a diseased patient. Chapter 5 provides details about the machine learning techniques used to detect lung cancer and pneumonia. Chapter 6 examines applications of deep learning techniques to fight the COVID-19 Pandemic. In Chapter 7, the use of deep autoencoders in the fields of bio-medicine is described with its implementation, and Chapter 8 covers chronic disease diagnosis using Artificial Intelligence and the Internet of Things. The last two chapters, Chapters 9 and 10 give focus to currently available health monitoring devices and possible improvements of their design along with the applications of IoRT (Internet of Robotics Things) in healthcare"-- Provided by publisher