Novel coronavirus disease (COVID-19): Emergence, early infection rate, and deployment strategies for preventive solutions

Novel coronavirus commonly known as coronavirus disease 19 (COVID-19) has rapidly spread worldwide and triggered the current global health crisis. It mostly affects humans through the zoonotic transmission of coronavirus 2 (SARS-CoV-2). This chapter focuses on the various epidemics and pandemics (plague, cholera, Spanish flu, etc.) in the history of human civilization, principal component analysis (PCA) for the interpretation of COVID-19 spreading kinetics during the first wave (in the year 2020), the potential use of herbal medicines, dietary remedies, and allopathic therapy to fight COVID-19, and various preventive measures undertaken to combat the pandemic during the first wave. The numbers of confirmed, recovered, active, and deceased cases is considered for the mapping of PCA within different countries. This study can be used as an informative approach for anticipating and strategy-making against COVID-19 or some other pandemics in the ensuing times. © 2022 Elsevier Inc. All rights reserved.

Masked face recognition using principal component analysis and deep learning

After COVID-19 pandemic most people all around the world are wearing face masks in public which makes traditional face recognition methods insufficient to recognize a face with mask. According to NIST2020 [1] report, face recognition accuracy of existing face recognition methods has a 20-50% of error rate due to the face covered by face mask, therefore, video surveillance systems and other face authentication systems are now not working efficiently. Recently many researchers have tried to solve this problem using mainly deep learning methods (CNN, FaceNet). Some researchers have used features from occluded (area covered by face mask) part and non-occluded part of the face while some researchers discard the occluded part and only used the non-occluded part of the face to recognize. For both of these two cases, the facial features of the occluded part of the face cannot be analyzed or cannot be used to recognize the face, as a result, these methods have high error rate. This paper proposed a reconstructive method to get partially reconstructed features of the occluded part of the face, then existing deep learning method has used to recognize the face. First the occluded part of a face is discarded and Principal component analysis (PCA) is applied to the remaining part of the face. Next, the principal components or the most significant Eigenvector and its corresponding weights are calculated to reconstruct the occluded part. Proposed method cannot reconstruct the occluded part completely but it can provide enough features of the occluded part which improves the recognition accuracy up to 15%. © 2021 IEEE.

Hatey Bazarey – A novel approach to ensure food security in lockdown period through an Android App

The adverse effects of the Novel Corona Virus or COVID – 19 on countries like United States of America, Italy, and Spain depicts how fast this virus can spread due to our irresponsible living habits. Till date, social distancing is the only solution to prevent the communal outspread of this pandemic. Other than fighting Corona, Government’s primary concern is to ensure proper food security for every citizen through Public Distribution System. But crowd management at such Fair Price Shops is a hectic job for the administration. Hence in this paper, we look forward to discussing a unique method of Public Distribution with the help of an Android App, named Hatey Bazarey, specially designed for Ration Dealers. Through this method, we aim to provide home delivery service of the lower-priced items as well as to keep a detailed record of the distributed items in a digital platform. © 2021 Institute of Physics Publishing. All rights reserved.