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.
Author(s): J. P. Patra, Gurudatta Verma
Series: Intelligent Signal Processing and Data Analysis
Publisher: CRC Press
Year: 2022
Language: English
Pages: 132
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgement
Author’s Biography
1 Cognitive Internet of Things and Its Impact on Human Life
1.1 Introduction to Cognitive Internet of Things
1.2 IoT
1.3 AI and IoT
1.4 Cognitive IoT and Covid-19 Pandemic
1.5 Global Applications of Cognitive IoT
1.6 Conclusion
References
2 Cognitive Internet of Things: Smart Student Evaluation
2.1 Education and Internet of Things
2.1.1 IoT and Education Institution
2.2 Machine Learning Classifiers for Smart Education
2.2.1 ML Classifiers
2.3 Implementation Using MATLAB Tool
2.3.1 Dataset Used and Curve Fitting Tool
2.3.2 Classification Learner Tool
2.4 Summary
2.4.1 Global Application ( Watson)
References
3 Cognitive Internet of Things: Chronic Disease Prediction
3.1 Chronic Disease and Human Health
3.1.1 Chronic Disease Monitoring
3.2 Disease Prediction and Machine Learning
3.2.1 Bottlenecks and Prediction Model
3.2.2 Naive Bayes Machine Learning Classifier
3.3 Heart Disease Prediction Using Matlab Tool
3.3.1 Dataset and Source
3.3.2 Classifiers and Accuracy
3.4 Summary
References
4 Challenges in Internet of Things: Energy-Efficient Wearables
4.1 Wearable Internet of Things
4.1.1 Wireless Body Area Network
4.1.2 WBAN Architecture
4.1.3 Body Sensors
4.1.4 WBAN Technologies
4.2 Issues and Challenges in WBAN
4.3 Localization in WBAN
4.4 WBAN and Earlier Study
4.4.1 What Is Not Being Done?
4.5 Applications
4.6 Limitations and Future Scope
References
5 Cognitive Internet of Things: Rainfall Prediction for Effective Farming
5.1 Farming and Cognitive Internet of Things
5.1.1 Gross Domestic Product and Agriculture
5.2 Machine Learning Model for Rainfall Prediction
5.2.1 Decision Tree Classifier
5.2.2 Support Vector Machine
5.2.3 Regression
5.3 Practical Approach ( Matlab Tool Box)
5.4 Summary
References
6 Cognitive Internet of Things: Lake Level Prediction to Prevent Drought
6.1 Data Forecasting and Boundaries
6.1.1 Motivation
6.2 Ensemble Prediction Model
6.2.1 Preprocessing
6.2.2 Ensemble Model
6.2.3 Ensemble Model Is Better Than Single Classifier
6.3 Validation of Prediction Model
6.4 Summary
References
Appendix: MATLAB Implementation of Different Classifiers
Index
