The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.

From the start of industrialization, air pollution in India and around the globe started increasing with new pollutants coming into existence. The extent of air pollution has also increased in the last few decades, very rapidly affecting human health and the earth's ecosystem badly. During pandemics like it happened in other countries, the same way, in India also government-imposed lockdown that resulted in the termination of all economic activities. The pandemic and imposed lockdown provided improved air quality as a gift to mankind. This duration also shows insight into the relationship between the economy and good air quality. We also get some insight into how to improve air quality without affecting the basic needs of society. This paper aims to examine the variation of the concentration of 5 main contaminants (PM2.5, PM10, NO2, CO and Ozone) before and after the lockdown imposed in 5 major Indian cities. After reading this paper, we get an insight into to what extent we can reduce air pollution by imposing lockdown and how the socio-economic, geographical variance across cities decides the extent of reduction in air pollution. We also get direction for future management strategies and policies to regain economic strength with sustainable improvements. So at the moment, we have to focus on both air pollution management and economic recovery. This study provides insight into the air quality index across the geographical variation of India.