ABSTRACT
LBPH (Local Binary Pattern Histogram) is a Facial recognition algorithm used to monitor a COVID infected person using a non-contact method of isolation check. The algorithm is programmed using Python software and the results are analysed using visual studio code. The program extracts feature from an input test image and compares it with the system database. The major goal would be to send the message if the person has violated the isolation norms. This algorithm captures the image of an isolated COVID patient when he/she breaks the isolation norms by opening the door and trying to escape from isolation. © 2022 IEEE.
ABSTRACT
Credit Card Fraud is one of the major threads in the financial industry. Due to the covid-19 pandemic and the advance in technologies, the number of users is increasing, with the increased use of credit cards. Due to more use of credit cards, Fraud cases also increase day by day. The research community striving hard to explore myriad credit card fraud detection techniques, but changes in technology and the varying nature of credit card fraud make it difficult to develop an effective technique for the detection of credit card fraud. This research work used a real-world credit card dataset. To detect the fraud transaction within this dataset three machine learning algorithms are used (i.e. Random Forest, Logistic regression, and AdaBoost) and compared the machine learning algorithms based on their Accuracy and Mathews Correlation Coefficient (MCC) Score. In these three algorithms, the Random Forest Algorithm achieved the best Accuracy and MCC score. The Streamlit framework is used to create the machine learning web application. © 2022 IEEE.