Corona virus disease (COVID-19), caused by SARS-CoV-2, is rapidly spreading all around the world and is posing a threat to mankind. Since SARS-CoV-2 is a novel virus, little is known about it and no effective drug is available for its treatment. While many drugs are being evaluated, an effective therapeutic measure is still lacking. SARS-CoV-2 like SARS-CoV binds with angiotensin-converting enzyme 2 (ACE2) present on human cells. SARS-CoV has been found to downregulate ACE2 and SARS-CoV-2 infection has been found to be associated with increased level of Angiotensin II. Based on these facts, we presume that SARS-CoV-2 like SARS-CoV downregulates ACE2, and in absence/reduced activity of ACE2, level of angiotensin (1-7) and angiotensin (1-9) is decreased while that of angiotensin II is increased and increased level of angiotensin II has been found to correlate with lung injury and viral load. We presume that restoration of normal functioning of renin-angiotensin system with recombinant human angiotensin-converting enzyme 2 (rhACE2), angiotensin (1-7) and angiotensin (1-9) may be an effective therapeutic measure but studies will be required to test this hypothesis and explore its possible role in treatment of COVID-19. Keywords COVID-19. Angiotensin-converting enzyme 2. rhACE2. SARS-CoV-2. Angiotensin This article is part of the Topical Collection on Covid-19

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

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