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.
Highlights
The COVID-19 pandemic is impacting many areas, including technology, education, the economy, and social life
Many of the works related to prediction of COVID-19 from patient records have used a few Machine learning (ML) algorithms regardless of performance accuracy, whereas this paper addresses the collection of real-time data with the help of Internet of Things (IoT) sensors to predict the risk of COVID-19 in public areas and analyze the performance of the leading ML algorithms to suggest the best algorithm based on performance accuracy in terms of prediction
COVID-19 is a community-acquired infection with symptoms resembling influenza and bacterial pneumonia
Summary
The COVID-19 pandemic is impacting many areas, including technology, education, the economy, and social life. The World Health Organization (WHO) recommends preventive measures such as social distancing and masks and gloves to limit the transmission of the virus from person to person. This study presents a system that recognizes interpersonal distances in crowds within a community or a queue in any organization to detect whether a person is wearing personal protective equipment such as a surgical mask and to detect any high body temperatures in a crowded place. This makes it possible to perform risk prediction in public areas. This study proposes the application of the Internet of Things (IoT) to collect all these input data to be processed by ML algorithms for classifying and predicting the risk based on the given data objects [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
