Awareness about COVID 19 regarding its Symptoms, Preventive Measures and compliance of Preventive Measures in rural and urban areas

Aim: To find out presence of awareness about COVID 19 in people of rural and urban areas Duration of study: 2 weeks until get maximum participation response Study design: Cross sectional survey Method: Questionnaires’ proforma was created from WHO and CDC website in google doc online form and was circulated among peoples of urban and rural areas through WhatsApp and Email. Responses were collected through google doc form and rearranged in form of tables/graph or pie chart. Results about knowledge of COVID 19 were presented in frequency and percentages. Conclusion: More awareness is needed about those COVID19 presentations, preventive measures and compliance of preventive measures which are being missed or not implemented by public so that spread of COVID 19 can be reduced and prevented

Defeating the Credit Card Scams through Machine Learning Algorithms

Credit card fraud is a significant problem that is not going to go away. It is a growing problem and surged during the Covid-19 pandemic since more transactions are done without cash in hand now. Credit card frauds are complicated to distinguish as the characteristics of legitimate and fraudulent transactions are very similar. The performance evaluation of various Machine Learning (ML)-based credit card fraud recognition schemes are significantly pretentious due to data processing, including collecting variables and corresponding ML mechanism being used. One possible way to counter this problem is to apply ML algorithms such as Support Vector Machine (SVM), K nearest neighbor (KNN), Naive Bayes, and logistic regression. This research work aims to compare the ML as mentioned earlier models and its impact on credit card scam detection, especially in situations with imbalanced datasets. Moreover, we have proposed state of the art data balancing algorithm to solve data unbalancing problems in such situations. Our experiments show that the logistic regression has an accuracy of 99.91%, and naive bays have an accuracy of 97.65%. K nearest neighbor has an accuracy is 99.92%, support vector machine has an accuracy of 99.95%. The precision and accuracy comparison of our proposed approach shows that our model is state of the art. © 2021 IEEE.