
Cognitive IoT
Description
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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.
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Persons
Mr Gurudatta Verma is Assistant Professor at Shri Shankaracharya Institute of Professional Management and Technology, Raipur, under Chhattisgarh Swami Vivekanand Technical University, Bhilai, India. He has more than 12 years of experience in research, teaching in the areas of parallel processing and machine learning. He has published more than 15 papers in SCOPUS, Web of Science, and UGC-CARE listed journals. He has published and granted Indian/Australian patents. He has contributed to book chapters published by Elsevier, Springer, and IGI Global.
Content
Abstract
1.1 Introduction to Cognitive IoT
1.2 Internet of Things
1.3 AI and IoT
1.4 Cognitive IoT and Covid19 Pandemic
1.5 Global Applications of Cognitive IoT
1.6 Conclusion
References
CHAPTER-2 COGNITIVE IOT: SMART STUDENT EVALUATION
Abstract
2.1 Education and IoT
2.2 Machine learning classifiers for Smart Education
2.3 Implementation using Matlab Tool
2.4 Summary
References
CHAPTER-3 COGNITIVE IOT: CHRONIC DISEASE PREDICTION
Abstract
3.1 Chronic Disease and Human Health
3.2. Disease prediction and Machine Learning
3.3 Heart Disease Prediction using MatLab Tool
3.4 Summary
References
CHAPTER-4 CHALLENGES IN IOT: ENERGY EFFICIENT WEARABLES
Abstract
4.1. Wearable IoT
4.2 Issues and Challenges in WBAN
4.3 Localization in WBAN
4.4. WBAN and Earlier Study
4.5. Applications
4.6. Limitations and Future Scope
References
CHAPTER-5 COGNITIVE IOT: RAINFALL PREDICTION FOR EFFECTIVE FARMING
Abstract
5.1 Farming and Cognitive IoT
5.2 Machine Learning Model for Rainfall Prediction
5.3 Practical Approach (Matlab Tool Box)
5.4 Summary
References
CHAPTER-6 COGNITIVE IOT: LAKE LEVEL PREDICTION TO PREVENT DROUGHT
Abstract
6.1 Data forecasting and Boundaries
6.2 Ensemble Prediction Model
6.3 Validation of Prediction Model
6.4 Summary
References
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