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Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning
- Source :
- Electronics, Vol 10, Iss 1677, p 1677 (2021), Electronics, Volume 10, Issue 14
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- COVID-19 is a community-acquired infection with symptoms that resemble those of influenza and bacterial pneumonia. Creating an infection control policy involving isolation, disinfection of surfaces, and identification of contagions is crucial in eradicating such pandemics. Incorporating social distancing could also help stop the spread of community-acquired infections like COVID-19. Social distancing entails maintaining certain distances between people and reducing the frequency of contact between people. Meanwhile, a significant increase in the development of different Internet of Things (IoT) devices has been seen together with cyber-physical systems that connect with physical environments. Machine learning is strengthening current technologies by adding new approaches to quickly and correctly solve problems utilizing this surge of available IoT devices. We propose a new approach using machine learning algorithms for monitoring the risk of COVID-19 in public areas. Extracted features from IoT sensors are used as input for several machine learning algorithms such as decision tree, neural network, naïve Bayes classifier, support vector machine, and random forest to predict the risks of the COVID-19 pandemic and calculate the risk probability of public places. This research aims to find vulnerable populations and reduce the impact of the disease on certain groups using machine learning models. We build a model to calculate and predict the risk factors of populated areas. This model generates automated alerts for security authorities in the case of any abnormal detection. Experimental results show that we have high accuracy with random forest of 97.32%, with decision tree of 94.50%, and with the naïve Bayes classifier of 99.37%. These algorithms indicate great potential for crowd risk prediction in public areas.
- Subjects :
- TK7800-8360
Computer Networks and Communications
Computer science
Internet of Things
Decision tree
02 engineering and technology
crowd analysis
Machine learning
computer.software_genre
Naive Bayes classifier
0202 electrical engineering, electronic engineering, information engineering
Isolation (database systems)
Electrical and Electronic Engineering
Artificial neural network
business.industry
Social distance
COVID-19
020206 networking & telecommunications
Random forest
Support vector machine
Identification (information)
machine learning
Hardware and Architecture
Control and Systems Engineering
Signal Processing
020201 artificial intelligence & image processing
Artificial intelligence
Electronics
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20799292
- Volume :
- 10
- Issue :
- 1677
- Database :
- OpenAIRE
- Journal :
- Electronics
- Accession number :
- edsair.doi.dedup.....472e429aa3f4341715e5f02a932b4215