AIoT and Big Data Analytics for Smart Healthcare Applications

Table of Contents

• Contents
• Preface
• Organization of the Book
• Acknowledgements
• List of Contributors

Chapter 1 a Survey on Semantic Aiot Concepts and Applications In

• Healthcare
• Sapna R. Pravinth Raja, Vidhya Banu, B. N. Shwetha and Shreyas Suresh Rao

1. Introduction
1.1. The Importance of Healthcare and Its Digitization
1.2. Internet of Things
1.3. Internet of Things and Health Care
1.4. Web of Things
2. Related Works
2.1. SWOT Layered Architecture
3. Semantic Technologies
3.1. Resource Description Framework (Rdf)
3.2. Sparql (Sparql Protocol and Rdf Query Language)
3.3. Ontology Web Language (Owl)
3.4. Semantic Web Rule Language (Swrl)
3.5. Shape Constraint Language (Shacl)
3.6. Ontology
4. Context and Entities
5. Methodologies and Assessment of Ontology
6. Semantic-Based Approaches for IoT
7. Wot Ontologies
8. Dealing With Heterogeneous Connected Environ- Ments
9. IoT Resource Management
10. Scalability With SWOT
11. SWOT Security Concerns

• Conclusion
• References

Chapter 2 IoT Based Sleeping Disorder Recognition System For

• Cognitive Impairment Diseases
• Nupur Choudhury, Rupesh Mandal and Jyoti Kumar Barman

1. Introduction
2. Background and Literature
3. General Framework of Big Data and IoT Based Osa Monitoring

• System

3.1. IoT Layer
3.1.1. Measurement of Sleep Parameters
3.1.2. Measurement of Physiological Parameters
3.1.3. Measurement of the Parameters Related to Physical Activity
3.1.4. Collection of Parameters for Air Pollution
3.2. Fog Layer
3.2.1. Interoperability and Communication
3.2.2. Event Processing
3.2.3. Event Handler
3.3. Cloud Layer

• Conclusion
• References

Chapter 3 Recent Trends in Smart Health Care: Past, Present And

• Future
• S. Kannadhasan, R. Nagarajan, R. Banupriya and Kanagaraj Venusamy

1. Introduction
2. Smart Health Care
3. Various Sector of Smart Health Care

• Conclusion
• References

Chapter 4 a Monitoring System for the Recognition of Sleeping

• Disorders in Patients With Cognitive Impairment
• Priya Dev and Abhishek Pathak

1. Introduction
1.1. Artificial Intelligence of Things (Aiot)
1.2. Big Data Analytics
2. The Methods and Approaches to Using Technology in Pandemic

• Situations Like Covid-19

3. The Advancements in Multi-Modal Sensors With Technologies To

• Monitor Physical Activity, Sleep, as Well as Circadian Rhythms

3.1. Variables Known to Impact Sleep Circadian Rhythms
4. Sleep-Wake Homoeostasis
5. Cognitive-Behavioural Effects
6. Quantifying Cognitive Impairment After Sleep Deprivation At

• Different Times of Day

7. Sleeping Disorders With Cognitive Impairment Diagnosis

• Through Medical Data Analysis

8. Applications in Implementing Aiot Enabled Solutions That Will

• Be Applied for the Treatment and Caring of Ill Patients

9. Artificial Intelligence With Multi-Functional Machine

• Learning Platform Development for Better Healthcare And
• Precision Medicine

10. Future Sleep Health

• Summary
• Conclusion
• References

Chapter 5 Early Prediction in Ai-Enabled IoT Environment

• Ambika N.

1. Introduction
2. Drawback of the Previous System
3. Literature Survey
4. Proposed Work
5. Analysis of the Work
5.1. Early Detection

• Conclusion
• References

Chapter 6 Ai and Blockchain-Based Solution for Implementing

• Security for Oral Healthcare 4.0 Big Data
• Sreekantha Desai Karanam, Niriksha Shetty and Rahul Bhandari

1. Introduction
1.1. Motivation - National and International Issues
1.2. Futuristic Solutions of Blockchain Solutions for Healthcare Industry
1.3. Market-Size for Blockchain Solutions in the Healthcare Industry
1.4. Taxonomy and Acronyms
1.5. Author's Research Contribution
2. Background and Related Work
2.1. Overview of Blockchain Technology in the Healthcare Industry
2.2. The Factors Driving the Growth of Blockchain in the Healthcare Industry
2.3. Related Work
2.3.1. Electronic Health Records Sharing
2.3.2. Ehr Claim and Billing Assessment
2.3.3. Clinical Research
2.3.4. Drug Supply Chain Management
2.3.5. Emr Application That Meets Onc Prerequisites
2.3.6. Comparative Study Features from Curated Survey Papers
3. Research Methodology for the Development of Blockchain-

• Based Web Portal

3.1. Data Processing
3.2. Data Collection
3.2.1. Enrichment of Data Before Storing Data in the Blockchain
3.2.2. Using the Blockchain to Store Medical Records
3.2.3. Smart Contracts and Data Consumption
3.3. Metamask
3.4. Truffle Suite
3.5. Ganache
3.6. Drizzle
4. Portal Wep Page Screen Layouts
4.1. Adding Patient Information
5. World Health Organization (Who) Specified Patient Data

• Collection Standards

5.1. The Architecture of Blockchain Based Healthcare Ecosystem
6. Managing Patient Data Using Blockchain Security Layer
7. Procedure to Access Patient Information

• Conclusion
• References

Chapter 7 An Artificial Intelligence-Based Method for Detecting

• False News in Health Sector During a Pandemic
• B. Sahana, B. Sadhana, Mamatha Mohan and Sindhu Rajendran

1. Introduction
1.1. Existing Problem
2. Algorithms
2.1. Logistic Regression
2.2. Decision Tree Classifier
2.3. Random Forest Classifier
2.4. Stochastic Gradient Descent
2.5. Xgb Classifier
2.6. Naive Bayes
3. Design Methodology
3.1. Collecting Dataset
3.2. Labelling the Data
3.3. Pre-Processing of Data

• - Tokenization
• - Stemming
• - Lemmatization
• - Stop Word Removal
• - Removing Punctuations

4. Count Vectorizer and Tf-Idf
4.1. Implementation of Data Pre-Processing
4.2. Machine Learning, Training and Building
4.3. Model Deployment Using Flask
5. Results
5.1. Performance Comparison of Various Models
6. Future Scope

• Conclusion
• References

Chapter 8 Intelligent Framework for Smart Health Application

• Using Image Analysis and Knowledge Relegation Approach
• Akhila Thejaswi R. Bellipady Shamantha Rai and Permanki Guthu Rithesh Pakkala

1. Introduction
2. Background
3. Materials and Methods
3.1. Data Collection and Manipulation
3.2. Training Phase
3.3. Testing Phase
3.4. Pseudo Codes Used
4. Results and Discussion

• Conclusion
• References

Chapter 9 Brain Stroke Prediction Using Deep Learning

• N.V. Maha Lakshmi, Sri Silpa Padmanabhuni, B. Hanumantha Rao, T. Krupa
• Nandini, T. Sai Teja and U. Vamsidhar Reddy

1. Introduction
2. Literature Survey
3. Proposed Methodology
3.1. Input Data
3.2. Data Preprocessing
3.3. Handling the Missing Values
3.4. Numerical Data
3.5. Categorical Data
3.6. Data Transformation
3.7. Splitting the Data
3.8. Model Building
3.9. Train Data
3.10. Test Data
3.11. Graphical Representation
3.12. Prediction
4. Results and Discussions

• Conclusion
• References

Author

• Shreyas Suresh Rao
• Steven Lawrence Fernandes
• Chandra Singh
• Rathishchandra R. Gatti