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Upadhyay, Nishant
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Credit card fraud detection using CNN and LSTM Upadhyay, Nishant; Bansal, Nidhi; Rastogi, Divya; Chaturvedi, Rekha; Asim, Mohammad; Malik, Suraj; Jayant, Khel Prakash; Vajpayee, Abhay Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1402-1410

Abstract

Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively.
Dynamic attendance system using face recognition via machine learning models Upadhyay, Nishant; Bansal, Nidhi; Velinov, Emil; Harshit, Harshit; Sharma, Abhay; Kumar, Sanjeev
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1421-1430

Abstract

Traditional methods to handle attendance have been implemented in the schools in the past and most of them are discouraging as they require that the institutions implement the use of paper and pen to get the results. To enhancing effectiveness and safeguarding, this paper presents a face recognition attendance system that mechanizes the usual attendance taking process. Using best practices in facial recognition, the system captures images of students’ faces, stores them, feeds them into a recognition model, and uses real-time facial recognition to mark attendance. This means that the system enjoys data encryption and password protected access that ensures data is safe. In the proposed system, the OpenCV face recognition libraries combined with machine learning algorithms for better face recognition ability with better efficiency. The results confirm that the system provides a reliable approach to handling attendance and it may debut in various contexts.
Co-Authors Asim, Mohammad Bansal, Nidhi Chaturvedi, Rekha Harshit, Harshit Jayant, Khel Prakash Kumar, Sanjeev Malik, Suraj Rastogi, Divya Sharma, Abhay Vajpayee, Abhay Kumar Velinov, Emil