References
- Itoo, F., & Singh, S. (2020). Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 1-9.
- Prusti, D., & Rath, S. K. (2019, July). Fraudulent transaction detection in credit card by applying ensemble machine learning techniques. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
- Maes, S., Tuyls, K., Vanschoenwinkel, B., & Manderick, B. (2002, January). Credit card fraud detection using Bayesian and neural networks. In Proceedings of the 1st international naiso congress on neuro fuzzy technologies (pp. 261-270).
- Raj, S. B. E., & Portia, A. A. (2011, March). Analysis on credit card fraud detection methods. In 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET) (pp. 152-156). IEEE.
- Charleonnan, A. (2016, October). Credit card fraud detection using RUS and MRN algorithms. In 2016 Management and Innovation Technology International Conference (MITicon) (pp. MIT-73). IEEE.
- Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017, October). Credit card fraud detection using machine learning techniques: A comparative analysis. In International Conference on Computing Networking and Informatics (ICCNI) (pp. 1-9). IEEE
- Randhawa, K., Loo, C. K., Seera, M., Lim, C. P., & Nandi, A. K. (2018). Credit card fraud detection using AdaBoost and majority voting. IEEE access, 6, 14277-14284 doi: https://doi.org/10.1109/ACCESS.2018.2806420
- Gyamfi, N. K., & Abdulai, J. D. (2018, November). Bank fraud detection using support vector machine. In IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp 37 – 41) IEEE
- Dighe, D., Patil, S., & Kokate, S. (2018, August). Detection of credit card fraud transactions using machine learning algorithms and neural networks: A comparative study. In Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-6). IEEE
- Kumar, P., & Iqbal, F. (2019, April). Credit Card Fraud Identification Using Machine Learning Approaches. In 1st International Conference on Innovations in Information and Communication Technology (ICIICT) (pp. 1-4). IEEE.
- Taha, A. A., & Malebary, S. J. (2020). An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access, 8, 25579-25587. doi: https://doi.org/10.1109/ACCESS.2020.2971354
- Karthikeyan, K., Raj, K. S., Ramaganesh, S., Parthasarathi, P., & Suguna, N. (2019). Credit Card Fraud Detection Using Machine Learning.
- Jonnalagadda, V., Gupta, P., & Sen, E. (2019). Credit card fraud detection using Random Forest Algorithm. International Journal of Advance Research, Ideas and Innovations in Technology (Volume 5, Issue 2).
- Lakshmi, S. V. S. S., & Kavilla, S. D. (2018). Machine learning for credit card fraud detection system. International Journal of Applied Engineering Research, 13(24 Pt. 1), 16819-16824.
- Vats, S., Sagar, B. B., Singh, K., Ahmadian, A., & Pansera, B. A. (2020). Performance evaluation of an independent time optimized infrastructure for big data analytics that maintains symmetry. Symmetry, 12(8), 1274 doi: https://doi.org/10.3390/sym12081274
- Vats, S., & Sagar, B. B. (2019). Performance evaluation of K-means clustering on Hadoop infrastructure. Journal of Discrete Mathematical Sciences and Cryptography, 22(8), 1349-1363. doi: https://doi.org/10.1080/09720529.2019.1692444
- Wang, C.Y., Zhao, W., Liu, Q. and Chen, H.W., 2017. Optimization of the tool selection based on big data. Journal of Discrete Mathematical Sciences and Cryptography, 20(1), pp.341-360. doi: https://doi.org/10.1080/09720529.2016.1183310
- Zhao, Y., 2018. Manufacturing personalization models based on industrial big data. Journal of Discrete Mathematical Sciences and Cryptography, 21(6), pp.1287-1292. doi: https://doi.org/10.1080/09720529.2018.1526403
- Tayyab Khan, Karan Singh, et. al, “ETERS: A comprehensive energy aware trust-based efficient routing scheme for adversial WSNs” Future Generation Computer Systems, 2021, https://doi.org/https://doi.org/10.1016/j.future.2021.06.049
- Mohd Shariq, Karan Singh, Maurya, P.K., et al. URASP: An ultralightweight RFID authentication scheme using permutation operation. Peer-to-Peer Netw. Appl. (2021). https://doi.org/https://doi.org/10.1007/s12083-021-01192-5
- Vats, S., & Sagar, B. B. (2020). An independent time optimized hybrid infrastructure for big data analytics. Modern Physics Letters B, 34(28), 2050311. doi: https://doi.org/10.1142/S021798492050311X
- Hanif M, Shahzad U, Shahzadi I and Koyuncu N, 2019. Stochastically increasing grouped data using the MLE of mean of the generalized exponential distribution. Taru Journal of Organizational Behavior & Analytics, 1 (1), pp. 65-82. doi: https://doi.org/10.47974/TJOBA.012.2019.v01i01
- Patil, H., Sharma, S. and Raja, L., 2021. Study of impact of COVID-19 on different age groups using machine learning classifiers. Journal of Interdisciplinary Mathematics, pp.1-9.