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IJIRTM: Volume-9, Issue-1, 2025

1
Paper Title : Credit Card Fraud Detection: Survey and Discussion
Author Name : Madhuree Sahu, Prof.Ritu Prasad
Keywords : Classification, On-line transactions, Machine learning, Automated teller machine.
Abstract :

Credit card fraud detection is a challenging problem that banks and credit card issuers are struggling with and are making huge efforts in implementing fraud detection systems. In today’s banking system, fraud detection is often done by using rule based methods. However, the progress and development of machine learning techniques gives banks and financial institutions the possibility to detect an unusual situation faster for big financial data sets. Machine Learning is a branch of Artificial Intelligence that has become very popular, and useful, in the last 10-15 years. One definition of Machine Learning is that it is the semi-automated extraction of knowledge from data. In this research work discusses different machine learning based credit/debit card fraud detection and challenges.

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2
Paper Title : Arrhythmia Data Classification Using QRS Detection and Support Vector Machines
Author Name : Deepak Rathore, Nitin Choudhary
Keywords : ECG, Arrhythmia Classification, QRS Peak Detection, R peak, FIR Filter, Pantompkins, SVM
Abstract :

Classification of arrhythmia patterns is an open field of research. One of the most widely used biological signals crucial for the detection of heart conditions is the electrocardiogram (ECG). Within the processing of ECG signals, one of the most critical aspects is the interpretation and acquisition of the QRS complex. Both the diagnosis of cardiac rhythm abnormalities and the assessment of heart rate variability depend heavily on the R wave (HRV). In this paper, the modified Pan-Tompkins method with an FIR filter is used for baseline wandering and noise reduction. Temporal statistical features are extracted using the QRS peak detection algorithm. The QRS interval, RR peak interval, QRS deviation, kurtosis, and skewness of the QRS are utilized as the features extracted from the ECG under test. This paper compares the classification accuracy of the standard QRS interval thresholding-based binary approach and the proposed support vector machine (SVM)-based classification method. The proposed optimized K-nearest neighbors (KNN) algorithm outperforms tree and thresholding-based classifiers.

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