Real Time Web-based Facemask Detection

The widespread spread of the Covid-19 virus in 2020-2021 is very worrying for all people around the world, coupled with the spread of a new variant of the Covid-19 virus, which is more aggressive and easily transmitted, causing public unrest about when this pandemic will end. The policy of using masks to reduce the spread of the virus has been made to minimize the spread. But even if there is a policy, there are still people who don't want to wear masks. Therefore, a mask detection system is needed to help differentiate whether someone uses a mask or not by displaying alerts in a form of web application. This research was conducted using several data augmentation techniques to increase the variation of the data to be used before training the algorithm model using the Convolutional Neural Network (CNN) algorithm with MobileNetV2 and VGG19 architectures. Both models are then evaluated where the architecture with the best performance will be implemented in the form of a web application. The accuracy of both models was compared, with the result of MobileNetV2 being 99% accurate and VGG19 being 98%. MobileNetV2 as the model that has the best accuracy value will be implemented in the form of a web application using the Haar Feature-Based Cascade to detect masks. The web application will be publicly accessed local at Universitas Multimedia Nusantara. © 2022 IEEE.

No one left behind: risk communication to the street vendors during COVID-19 social distancing

Purpose COVID-19 presents a serious and unprecedented challenge around the globe. Street vendors are the most vulnerable group during this pandemic regarding livelihood loss and contagion risk. This research aims to examine the roles of risk communication work in enhancing COVID-19 risk perceptions and adoption of COVID-19 preventive behaviors among street vendors. Design/methodology/approach The data were collected from the street vendors in urban Vietnam. A binary probit model was used for analyzing the relationships among exposure to risk communication, risk perception and adoption of preventive behaviors. Findings The analysis reveals the outreach of risk communication work to the street vendors. A rather large proportion of the respondents perceive high risks associated with COVID-19. All respondents adopt COVID-19 preventive behaviors;however, the proportion of regular adoption is moderate and even very low for most behaviors. Their frequent exposure to risk communication significantly raises their risk perceptions and encourages their regular adoption of preventive behaviors, particularly regarding the measures that are affordable and less detrimental to their livelihood. Originality/value This research is among the first attempts to examine exposure to risk communication to the vulnerable group, how they perceive risks and the extent to which they adopt preventive behaviors during a public health crisis. This research draws some implications for risk communication and social welfare policies to obtain sustainable development goals.