Improved Credit Card Fraud Prediction using Edited Nearest Neighbors Learning Technique
DOI:
https://doi.org/10.26438/ijcse/v11i8.6570Keywords:
Credit Card, Deep Learning, ENN, DNN, AccuracyAbstract
Cloud computing and mobile computing have increasing its performance with rapid manner through numerous area of applications, these are extending such as digital payments, storage and confidential information accessing. Current technology offers several internet applications by using cloud based electronic payment methods, therefore security and confidentiality is necessary. According to national herald in India 42% frauds are identified in various fields from 1990 to 2020. Like “no fraud” agency in USA identified around 30% frauds since 1990, every year these frauds are increases with high ratios. Frauds did not have particular patterns, also change their behavior at every time. These frauds are most probably recognized at cloud based e-commerce and trade business websites. A real and precise fraud detection system must be developed in order to reduce this fraud ratio. In this exploration with the assistance of profound and AI improvement strategies has been utilized to recognize the cloud based fakes. So many, existed works settle this issue yet precision, F-score, review and precession are exceptionally less. Due to this impediment, in this work is introduced deep learning mechanisms like fully Edited Nearest Neighbor (ENN) and deep neural network (DNN). The DNN with ENN is best technique for credit card fraud prediction and achieve good accuracy.
References
[1] Tesfahun Berhane , Tamiru Melese, Assaye Walelign, and Abdu Mohammed, “A Hybrid Convolutional Neural Network and Support Vector Machine-Based Credit Card Fraud Detection Model”, Mathematical Problems in Engineering (SCI), Hindawi, pp.1-10, 2023.
[2] Ibomoiye Domor Mienye and Yanxia Sun, “A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection”, Applied Science, 2023.
[3] Vipul Jain, H Kavitha and S Mohana Kumar, “Credit Card Fraud Detection Web Application using Streamlit and Machine Learning”, International Conference on Data Science and Information System (ICDSIS), IEEE 2022.
[4] Yathartha Singh, Kiran Singh and Vivek Singh Chauhan, “Fraud Detection Techniques for Credit Card Transactions”, 3rd International Conference on Intelligent Engineering and Management (ICIEM), IEEE 2022.
[5] J. Leach and U. Tayasivam, “Optimizing data evaluation metrics for fraud detection using machine learning,” International Journal of Mathematics and Computer Science, Vol.16, Issue.8, pp.52–59, 2022.
[6] R. Bin Sulaiman, V. Schetinin, and P. Sant, “Review of machine learning approach on credit card fraud detection,” Human-Centric Intelligent Systems, Vol.2, Issue.1-2, pp.55–68, 2022.
[7] V. Plakandaras, P. Gogas, T. Papadimitriou, and I. Tsamardinos, “Credit card fraud detection with automated machine learning systems,” Applied Artifcial Intelligence, vol. 36, no. 1, Article ID 2086354, 2022.
[8] E. F. Malik, K. W. Khaw, B. Belaton, W. P. Wong, and X. Chew, “Credit card fraud detection using a new hybrid machine learnig Architecture”, Mathematics, Vol.10, Issue.9, pp.1480, 2022.
[9] S. Al-Faqir and O. Ouda, “Credit card frauds scoring model based on deep learning ensemble,” Journal of Teoretical and Applied Information Technology, Vol.100, Issue.14, 2022.
[10] F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” IEEE Access, Vol.10, pp.39700–39715, 2022.
[11] F. Itoo and S. Singh "Comparison and analysis of logistic regression Naïve Bayes and KNN machine learning algorithms for credit card fraud detection" International Journal of Information Technology Vol.13, Issue.4, pp.1503-1511 2021.
[12] E. S. C. R. S K Saddam Hussain "Fraud Detection in Credit Card Transactions Using SVM and Random Forest Algorithms" IEEE Xplore 2021.
[13] E. Ileberi Y. Sun and Z. Wang "Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost" IEEE Vol.9, Issue.5, pp.165286-165294 2021.
[14] D. Tanouz R. R. Subramanian and D. Eswar "Credit Card Fraud Detection Using Machine Learning" IEEE 2021.
[15] S. Khatri A. Arora and A. P. Agrawal "Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison" IEEE 2020.
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