A System Design with Deep Learning and IoT to Ensure Education Continuity for Post-COVID

In this paper, an IoT and deep learning-based comprehensive study to reduce the effects of COVID-19 on the education system is presented. The proposed system consists of an edge device, IoT nodes, and a neural network that runs on a server. The purpose of the proposed system is to protect students and staff against infectious diseases and increase the students performance during classes by monitoring the environmental conditions via an IoT-based sensor network, during the current pandemic to ensure the use of masks in closed areas by training a customized deep learning model, and to monitor the student attendance data by deep learning and IoT-based solution. Furthermore, effective heating and cooling can be done to save energy by transmitting the environmental conditions of the indoor environment to the relevant destinations. The experiment is conducted with five different networks to classify the faces in the images as masked or unmasked, and their performances were examined. The networks were trained on the Face Mask Detection Dataset which contains a total of 7553 masked and unmasked images. The best results were obtained as 99.5% for the F1 Score and 99% for MCC by the model trained on the InceptionV3 network. IEEE

A System Design for Monitoring the Violation of Home Quarantine

Due to the rapid spread of mutated variants of coronavirus, and the lack of complete mass vaccination all over the world, everybody needs to strict adherence to personal hygiene and social distancing. And also, contamination of infected individuals for preventing coronavirus spreading plays a vital role. Home quarantine seems to be the best isolation solution in terms of cost and psychological health of individuals who are infected or close contacts. However, the number of quarantine violations increases day by day in many parts of the world because of the long duration of the pandemic. In this paper, a potential solution is presented for monitoring the violation of home quarantine based on the Internet of Things (IoT) and cloud computing technologies. The deep learning convolutional neural network is trained on the cloud with images of quarantine subjects for face recognition. Monitoring IoT nodes expect individuals to scan their faces at regular intervals to prevent them from leaving quarantine areas. Moreover, by measuring their body temperature regularly, unexpected rises can be detected, and this situation can be reported to health authorities. IEEE

Can IoMT help to prevent the spreading of new coronavirus?

This paper presents a potential solution based on the Internet of Medical Things for the COVID-19 epidemic which determines the symptoms of the disease without required any user action and warns the user. In medical studies on COVID-19 in the literature, the most common symptoms encountered since the onset of COVID-19 disease have been reported as fever, dry cough, fatigue, anorexia, anosmia, dyspnea. The average incubation period is 5 days;therefore, it is very important to detect the symptoms even before the infected people notice the signs, and warn them. Thus, it can be ensured that they isolate themselves from society against the possible disease condition and prevent the spread of the disease. A wrist-worn device that does not physically disturb the person is proposed to track the health conditions of the person and detect the symptoms of COVID-19 at the early stages. IEEE