Abstract: The COVID-19 pandemic has had catastrophic consequences all over the world since the detection of the first case in December 2019. Currently, exponential growth is expected. In order to stop the spread of this pandemic, it is necessary to respect sanitary protocols such as the mandatory wearing of masks. In this research paper, we present an affordable artificial intelligence-based solution to increase the protection against COVID-19, covering several relevant aspects to facilitate the detection and prevention of this pandemic: non-contact temperature measurement, mask detection, automatic gel-dispensing, and automatic sterilization. Our main contribution is to provide high-quality, real-time learning and analysis. To achieve this goal, we used a deep convolutional neural network (CNN) based on MobileNetV2 architecture as the learning algorithm and Advanced Encryption Standard (AES) as an encryption protocol for sending secure data to notify hospital staff. The experimental results show the effectiveness of our model by providing 99.7% accuracy in detecting masks with a runtime of 1.54 s.

Sofien Chokri, Wided Ben Daoud, Wasma Hanini, Sami Mahfoudhi and Amel Makhlouf, “AI-based System for the Detection and Prevention of COVID-19” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130171

@article{Chokri2022,
title = {AI-based System for the Detection and Prevention of COVID-19},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130171},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130171},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Sofien Chokri and Wided Ben Daoud and Wasma Hanini and Sami Mahfoudhi and Amel Makhlouf}
}