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The outbreak of Coronavirus Disease (COVID-19) has caused a huge disturbance globally. The problem is the unavailability of vaccines and limited resources for its detection. In this paper, authors have carried out a case study of India to analyse the problem faced by the authorities for detecting COVID-19 amongst the suspected cases and have tried to solve the problem using a Deep Neural Networkbased approach for analyzing chest x-rays in order to detect the onset/presence of related disease. After obtaining data from available resources, we trained a transfer learning-based CNN model. The model tries to extract the features of the radiographs and thus classifies it into the appropriate class. Heat map filter was used on the images significantly helping the model to perform better. This paper presents the validation of the model on certain test images and shows that the model is reliable to an extent. This paper also demonstrates a general architecture for the deployment of the model as per the considered case study. Keywords Deep learning • Radiography • Feature extraction • COVID-19 • Heatmap filter • CNN Recently, the outbreak of Coronavirus Disease proved to be disastrous due to the exponentially increasing number of cases worldwide. Not only the confirmed cases are increasing but also the number of deaths across the world seems to increase exponentially. As of early April 2020, around 1.34 million cases have been

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There is a crucial need for advancement in the online educational system due to the unexpected, forced migration of classroom activities to a fully remote format, due to the coronavirus pandemic. Not only this, but online education is the future, and its infrastructure needs to be improved for an effective teaching-learning process. One of the major concerns with the current video call-based online classroom system is student engagement analysis. Teachers are often concerned about whether the students can perceive the teachings in a novel format. Such analysis was involuntarily done in the offline mode, however, is difficult in an online environment. This research presents an autonomous system for analyzing the students' engagement in the class by detecting the emotions exhibited by the students. This is done by capturing the video feed of the students and passing the detected faces to an emotion detection mode. The emotion detection model in the proposed architecture was designed by finetuning VGG16 pre-trained image classifier model. Lastly, the average student engagement index is calculated. We received considerable performance setting reliability of the use of the proposed system in real-time giving a future scope to this research.