[1] 8ir5Sakharova, I. (2012, June). Payment card fraud: Challenges and solutions. In 2012 IEEE international conference on intelligence and security informatics (pp. 227-234). IEEE
[2] Almazroi, A. A., & Ayub, N. (2023). Online Payment Fraud Detection Model Using Machine Learning Techniques. IEEE Access, 11, 137188-137203
[3] El Naqa, I., & Murphy, M. J. (2015). What is machine learning? (pp. 3-11). Springer International Publishing.
[4] Minastireanu, E. A., & Mesnita, G. (2019). An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection. Informatica Economica, 23(1).
[5] Nasr, M. H., Farrag, M. H., & Nasr, M. M. (2022). A Proposed Fraud Detection Model based on e-Payments Attributes a Case Study in Egyptian e-Payment Gateway. International Journal of Advanced Computer Science and Applications, 13(5).
[6] Fang, Y., Zhang, Y., & Huang, C. (2019). Credit Card Fraud Detection Based on Machine Learning. Computers, Materials & Continua, 61(1).
[7] Mijwil, M. M., & Salem, I. E. (2020). Credit card fraud detection in payment using machine learning classifiers. Asian Journal of Computer and Information Systems (ISSN: 2321–5658), 8(4).
[8] Adepoju, O., Wosowei, J., & Jaiman, H. (2019, October). Comparative evaluation of credit card fraud detection using machine learning techniques. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-6). IEEE.
[9] Isabella, S. J., Srinivasan, S., & Suseendran, G. (2020). An efficient study of fraud detection system using Ml techniques. Intelligent Computing and Innovation on Data Science, 59.
[10] Pumsirirat, A., & Liu, Y. (2018). Credit card fraud detection using deep learning based on auto-encoder and restricted boltzmann machine. International Journal of advanced computer science and applications, 9(1).
[11] Corballis, M. C., & Nagourney, B. A. (1978). Latency to categorize disoriented alphanumeric characters as letters or digits. Canadian Journal of Psychology/Revue canadienne de psychologie, 32(3), 186.
[12] Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.
[13] Asim, M., & Zakria, M. (2020). Advanced kNN: A Mature Machine Learning Series. arXiv preprint arXiv:2003.00415.
[14] Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision support systems, 50(3), 559-569.
[15] Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., ... & Cheng, C. Y. (2020). Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of clinical epidemiology, 122, 56-69.
[16] Ravisankar, P., Ravi, V., Rao, G. R., & Bose, I. (2011). Detection of financial statement fraud and feature selection using data mining techniques. Decision support systems, 50(2), 491-500.
[17] Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert systems with applications, 134, 93-101.
[18] Ding, S., Su, C., & Yu, J. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial intelligence review, 36, 153-162.
[19] Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert systems with applications, 32(4), 995-1003.
[20] West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & security, 57, 47-66.
[21] Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361.