Evolving Role of AI and IoMT in the Healthcare Market

About the Book
Contents
About the Editors
Chapter 1: A Study of Time Series Forecasting Techniques for COVID-19 Trends
1.1 Introduction
1.2 Data Source
1.3 Exploratory Data Analysis
1.3.1 Pearson´s Correlation Coefficient
1.3.2 Autocorrelation
1.4 Data Cleaning and Preparation
1.4.1 NaN
1.4.2 Missing Values
1.5 Forecasting Models
1.5.1 Baseline Models
1.5.1.1 Mean-Based Forecasting
1.5.1.1.1 Theory
1.5.1.1.2 Application to Data (Fig. 1.3)
1.5.1.1.3 Remarks
1.5.1.2 Naïve Forecasting
1.5.1.2.1 Theory
1.5.1.2.2 Application to Data (Fig. 1.4)
1.5.1.2.3 Remarks
1.5.1.3 Drift-Based Forecasting
1.5.1.3.1 Theory
1.5.1.3.2 Application to Data (Fig. 1.5)
1.5.1.3.3 Remarks
1.5.2 Exponential Smoothing Models
1.5.2.1 Single Exponential Smoothing
1.5.2.1.1 Theory
1.5.2.1.2 Application to Data (Fig. 1.6)
1.5.2.1.3 Remarks
1.5.2.2 Double Exponential Smoothing
1.5.2.2.1 Double Exponential Smoothing: Additive Trend
Theory
Application to Data (Figs. 1.7 and 1.8)
Remarks
1.5.2.2.2 Double Exponential Smoothing: Multiplicative Trend
Theory
Application to Data (Figs. 1.9 and 1.10)
Remarks
1.5.2.3 Triple Exponential Smoothing
1.5.3 ARIMA
1.5.3.1 Theory
1.5.3.2 Application to Data (Fig. 1.11)
1.5.3.3 Remarks
1.5.4 LSTM
1.5.4.1 Theory
1.5.4.2 Application to Data (Fig. 1.12)
1.5.4.3 Remarks
1.6 Evaluation and Comparison of Forecasting Models
1.6.1 Performance Metrics
1.6.2 Model Evaluation and Comparison
1.6.2.1 Time Series 1: India: Total Cases
1.6.2.2 Time Series 2: India: New Cases
1.6.2.3 Time Series 3: China: Total Cases
1.6.2.4 Time Series 4: China: New Cases
1.6.2.5 Time Series 5: United States: Total Cases
1.6.2.6 Time Series 6: United States: New Cases
1.7 Limitations
1.7.1 Limitations in Data
1.7.2 Limitations in Modelling
1.8 Conclusion
References
Chapter 2: EEG Analysis Using Bio-Inspired Metaheuristic Approach
2.1 Introduction
2.2 Generalized System Model for Neurological Disease Detection
2.3 Metaheuristic Approaches
2.4 Application of BI Approaches in Neurological Disease Detection
2.4.1 Alzheimer´s Disease Detection Using BI
2.4.2 Autism Disease Detection Using BI
2.4.3 Parkinson Disease Detection Using BI
2.4.4 Epilepsy Disease Detection Using BI
2.5 Conclusion
References
Chapter 3: Secure Recommendation System for Healthcare Applications Using Artificial Intelligence
3.1 Introduction
3.2 Secure Recommendation System
3.2.1 Cloud Users with Different Interests
3.2.2 Trusted Third Party
3.2.3 Cloud Service Provider
3.2.4 Cloud Users with the Same Interest
3.3 Tag Matching Mechanism
3.3.1 Setup
3.3.2 Key Generation
3.3.3 Communication Key Generation
3.3.4 Encryption
3.3.5 Decryption
3.4 Security Analysis
3.4.1 Impersonation Attack
3.4.2 Replay Attack
3.4.3 Man in the Middle Attack
3.4.4 Eavesdropping
3.4.5 DDoS Attack
3.5 Experimental Analysis
3.5.1 Phase of Key Computation
3.5.2 Phase of Encryption
3.5.3 Phase of Decryption
3.5.4 Communication Complexity
3.6 Conclusions and Future Works
References
Chapter 4: IoT Based Healthcare: A Review
4.1 Introduction
4.2 Literature Review
4.3 Applications of IoT in Healthcare
4.3.1 IoT Based Diabetes Management
4.3.2 IoT Devices for Asthma Management
4.3.3 IoT for Mental Health
4.3.4 IoT Role in Pandemic Situation
4.3.5 IoT for Sleep Disorder
4.4 Working of IOT Devices
4.5 Role of Cloud in IoT Based Healthcare
4.6 Benefits and Challenges
4.7 Conclusion and Future Scope
References
Chapter 5: Diagnosing Alzheimer´s Disease Using Deep Learning Techniques
5.1 Introduction
5.2 Brain Structure
5.2.1 Cerebral Cortex
5.2.2 Corpus Callosum
5.2.3 Cerebellum
5.2.4 Brain Stem
5.2.5 Limbic System
5.2.6 Amygdala
5.2.7 Hippocampus
5.2.8 Thalamus
5.2.9 Hypothalamus
5.3 Alzheimer Disease-Introduction
5.3.1 Some Statistics
5.3.2 Impairments in Alzheimer Disease
5.4 Alzheimer´s Disease Vs. Dementia Vs. Normal Aging
5.4.1 Stages of Alzheimer
5.5 Different Procedure to Find Alzheimer Disease
5.5.1 Image Capturing
5.5.2 Cerebrospinal Fluid (CSF) Procedure
5.5.3 Deep Learning Techniques
5.5.3.1 Activation Function
5.5.3.1.1 Step Function
5.5.3.1.2 Sigmoid Function
5.5.3.1.3 Tanh Function
5.5.3.1.4 ReLU Function
5.5.3.1.5 Neural Networks
5.5.3.1.6 Convolutional Neural Networks (CNN)
5.5.3.1.7 Pooling
5.5.3.1.8 Recurrent Neural Networks (RNN)
5.6 Deep Learning Methods Comparison
5.7 Conclusion
References
Chapter 6: Artificial Intelligence and Blockchain: The Future of Healthcare
6.1 Introduction
6.1.1 Introduction to Artificial Intelligence
6.1.2 How Does Artificial Intelligence Work?
6.1.3 Uses of A.I.
6.1.3.1 Narrow A.I.
6.1.3.2 Artificial A.I.
6.1.4 Components of Artificial Intelligence
6.1.5 Artificial Intelligence and Healthcare
6.1.5.1 Various Areas of Expertise in Medicine Have Demonstrated an Improvement in Studies on A.I.
6.1.5.1.1 Radiology
6.1.5.1.2 Psychiatry
6.1.5.1.3 Screening
6.1.5.1.4 Disease Diagnosis
6.1.5.1.5 Telehealth
6.1.5.1.6 Electronic Health Record
6.1.5.1.7 Primary Care
6.1.5.1.8 Drug Interaction
6.1.5.1.9 Robo Dentist
6.1.5.1.10 AI Doctors
6.1.5.1.11 Non-adherence
6.2 Introduction to Blockchain
6.2.1 Features of Blockchain
6.2.1.1 Accuracy of Chain
6.2.1.2 Cost Reductions
6.2.1.3 Decentralization
6.2.1.4 Private Transactions
6.2.1.5 Efficient Transactions
6.2.1.6 Transparency
6.2.1.7 Secure Transactions
6.2.2 Types of Blockchain
6.2.2.1 Public Blockchain
6.2.2.2 Sidechains
6.2.2.3 Proprietary Blockchain
6.2.2.4 Hybrid Blockchain
6.2.3 Uses of Blockchain
6.2.3.1 Cryptocurrency
6.2.3.2 Financial Services
6.2.3.3 Domain Names
6.2.3.4 Video Games
6.2.4 Blockchain and Healthcare
6.2.5 Companies Installed Blockchain
6.2.5.1 BurstIQ
6.2.5.2 Medical Chain
6.2.5.3 Factom
6.2.5.4 Guardtime
6.2.6 Medical Records and Health Plans
6.2.6.1 Simply Vital Health
6.2.6.2 Robomed
6.2.6.3 Coral Health
6.2.6.4 Patientory
6.2.7 Supply Chain Management Associated with Blockchain
6.2.7.1 Blockpharma
6.2.7.2 Chronicled
6.2.7.3 Tierion
6.2.7.4 CDC
6.2.8 Medical Credential Tracking
6.2.8.1 Drug Trials
6.2.8.2 Payment Through Crypto
6.2.8.3 Access of Medical Records
6.3 Conclusion
References
Chapter 7: Role of Artificial Intelligence for Skin Cancer Detection
7.1 Introduction
7.2 Related Works
7.2.1 Detection and Analysis of the Type of Skin Cancer
7.2.2 HCI: Human-Computer Interaction
7.3 Comparison Table (Tables 7.1 and 7.2)
7.4 Conclusion
References
Chapter 8: Evolving IoT and Green IoT in Healthcare Perspective
8.1 Introduction
8.1.1 Genesis of IoT
8.2 Advantages of IoT
8.2.1 Information
8.2.2 Tracking
8.2.3 Time
8.2.4 Money
8.3 Challenges of IoT Implementation
8.3.1 Technology
8.3.1.1 Security
8.3.1.2 Connectivity
8.3.1.3 Compatibility and Longevity
8.3.1.4 Standards
8.3.1.5 Intelligent Analysis
8.4 Application of IoT
8.4.1 Digital Ceiling
8.5 Architecture of IoT
8.5.1 Physical Device and Controllers (The Things of IoT)
8.5.2 Connectivity (Communication and Processing Unit)
8.5.3 Edge Computing
8.5.4 Data Accumulation (Storage)
8.5.5 Data Abstraction (Aggregation Access)
8.5.6 Application (Reporting, Analytics, Control)
8.5.7 Collaboration Process (Involving Process of People and Business)
8.6 IoT in Healthcare Domain
8.6.1 Future Success of IoT in Healthcare
8.7 Application of Healthcare IoT
8.7.1 U-Healthcare IoT
8.7.2 Automatic/Controller Wheelchair
8.7.3 WBAN
8.8 Green IoT
8.9 Energy Efficient Approaches for Enabling Green IOT
8.9.1 Industrial Automation
8.9.1.1 Smart Industrial Plants
8.9.1.2 Smart Plant Monitoring
8.9.2 Health and Living
8.9.2.1 Real-Time Following
8.9.2.2 Smart Information Assortment
8.9.3 Habitat Monitoring
8.9.3.1 Smart Animal
8.9.3.2 Smart Building
8.9.4 Energy
8.9.5 Transportation
8.9.5.1 Smart Parking
8.9.5.2 Smart Traffic
8.10 Green IoT Association in Healthcare
8.10.1 Existing WBAN Technologies
8.10.2 Existing Solar Energy Harvesting of IoT
8.10.2.1 Green Energy Wireless Charging
8.10.2.2 Photovoltaic Cell Energy Harvesting
8.11 Conclusion
8.12 Future Scope
References
Chapter 9: A Review in Wavelet Transforms Based Medical Image Fusion
9.1 Introduction
9.2 Wavelet Transform
9.2.1 Discrete Wavelet Transform (DWT)
9.2.2 Multiresolution Analysis
9.3 Wavelet and Bio Inspired Computation Based Fusion
9.4 Result and Discussions
9.5 Conclusion
References
Chapter 10: Cloud-Based Intelligent Internet of Medical Things Applications for Healthcare Systems
10.1 Introduction
10.1.1 Smart Ambulance
10.1.2 Managing Hospitals Medical Record
10.1.3 Digital Consultation
10.1.4 Virtual Nurse
10.1.5 Prediction for the Risk of Coronary Heart Disease
10.1.6 Human Sitting Posture Recognition System
10.2 Conclusion
References
Chapter 11: Development of Intelligent Approach to Detect Retinal Microaneurysm
11.1 Introduction
11.1.1 Diabetic Retinopathy Stages
11.1.2 Techniques of Fundus Imaging
11.1.2.1 Red-Free Fundus Photography
11.1.2.2 Colour Fundus Imaging
11.1.2.3 Fundus Autofluorescence
11.1.3 Retinal Imaging
11.1.4 Modalities of Retina Fundus Imaging
11.1.4.1 Standard View
11.1.4.2 Ultra-Wide Field
11.1.4.3 Smartphone-Based Images
11.2 Related Works
11.3 Methodology
11.3.1 Pre-Processing
11.3.2 Mapping of Images to Text
11.3.3 MA Detection Using Multi Sieving Convolution Neural Networks
11.3.4 Probabilistic Neural Networks
11.4 Result and Discussion
11.4.1 Findings
11.5 Conclusion and Future Work
References
Chapter 12: Automatic Brain Tumor Detection Using Machine Learning and Mixed Supervision
12.1 Introduction
12.2 Preliminary Discussion
12.2.1 Why Do We Choose MRI for Detecting Brain Tumors
12.2.2 Methods
12.2.2.1 Manual Segmentation
12.2.2.2 Semi-Automatic Segmentation
12.2.2.3 Fully Automatic Segmentation
12.2.3 Problem Statement
12.2.4 Image Segmentation Techniques
12.2.4.1 Threshold Feature-Based Segmentation
12.2.4.2 Region-Based Segmentation
12.2.4.3 Feature-Based Clustering
12.2.4.4 Edge-Based Segmentation
12.3 Methodology
12.3.1 Preprocessing
12.3.2 Enhancement
12.3.3 Skull Stripping
12.3.4 Feature Extraction
12.3.5 Tumor Classification Using KNN
12.4 Results
12.5 Conclusion
References
Chapter 13: Architecture for Multisensor Fusion and Integration for Diabetes Monitoring
13.1 Introduction to Wireless Sensor Network
13.1.1 Architecture
13.1.2 Characteristics
13.2 Wireless Sensor Network in Health Care
13.2.1 Open Problems
13.3 Glucose Monitoring
13.3.1 Requirement
13.3.2 Existing Architecture
13.4 Error Correction and Prediction
13.5 Results and Discussion
13.6 Scope for Future Work
References
Index