Open Access
Issue
E3S Web Conf.
Volume 453, 2023
International Conference on Sustainable Development Goals (ICSDG 2023)
Article Number 01015
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202345301015
Published online 30 November 2023
  1. Lucas Y, Portier P-E, Laporte L, et al. Multiple perspectives HMM-based feature engineering for credit card fraud detection. In: ACM, 2019. p. 1359–1361. [Google Scholar]
  2. Duman E, Elikucuk I. Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization. Berlin: Springer; 2013. [Google Scholar]
  3. Botchey FE, Qin Z, Hughes-Lartey K. Mobile money fraud prediction— a cross-case analysis on the efficiency of support vector machines, gradient boosted decision trees, and Naïve Bayes algorithms. Information. 2020;11:383. https://doi.org/10.3390/info11080383. [Google Scholar]
  4. Ogwueleka FN. Data mining application in credit card fraud detection system. J Eng Sci Technol. 2011;6:311–22. Sriram Sasank JVV, Sahith GR, Abhinav K, Belwal M. Credit Card fraud detection using various classification and sampling techniques: a comparative study. In: IEEE, 2019. p. 1713–1718. [Google Scholar]
  5. Ojugo AA, Nwankwo O. Spectral-cluster solution for credit-card fraud detection using a genetic algorithm trained modular deep learning neural network. JINAV J Inf Vis. 2021;2:15–24. https://doi.org/10.35877/454RI.jinav274. [CrossRef] [Google Scholar]
  6. Majhi SK, Bhatachharya S, Pradhan R, Biswal S. Fuzzy clustering using SALP swarm algorithm for automobile insurance fraud detection. J Intell Fuzzy Syst. 2019;36:2333–44. https://doi.org/10.3233/JIFS169944. [CrossRef] [Google Scholar]
  7. Darwish SM. An intelligent credit card fraud detection approach based on semantic fusion of two classifiers. Soft Comput. 2019;24:1243–53. https://doi.org/10.1007/s0050001903958-9. [Google Scholar]
  8. C. H. Sumanth, P. P. Kalyan, B. Ravi and S. Balasubramani., “Analysis of Credit Card Fraud Detection using Machine Learning Techniques, ” 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2022, pp.1140-1144. doi:10.1109/ICCES54183.2022.9835751 [Google Scholar]
  9. F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan and M. Ahmed, “Credit Card Fraud Detection Using State-ofthe-Art Machine Learning and Deep Learning Algorithms, ” in IEEE Access, vol. 10, pp. 39700-39715, 2022. doi:10.1109/ACCESS.2022.3166891 [CrossRef] [Google Scholar]
  10. J. C. Mathew, B. Nithya, C. R. Vishwanatha, P. Shetty, H. Priya and G. Kavya, “An Analysis on Fraud Detection in Credit Card Transactions using Machine Learning Techniques, ” 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2022, pp. 265-272. doi:10.1109/ICAIS53314.2022.9742830 [Google Scholar]
  11. J. B, J. A. K. R and D. P. S. Ganesh, “Credit Card Fraud Detection with Unbalanced Real and Synthetic dataset using Machine Learning models, ” 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India, 2022, pp. 73-78. doi:10.1109/ICESIC53714.2022.9783529 [Google Scholar]
  12. D. Tanouz, R. R. Subramanian, D. Eswar, G. V. P. Reddy, A. R. Kumar and C. V. N. M. Praneeth, “Credit Card Fraud Detection Using Machine Learning, ” 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 967-972. doi:10.1109/ICICCS51141.2021.9432308 [Google Scholar]
  13. A. S. Rathore, A. Kumar, D. Tomar, V. Goyal, K. Sarda and D. Vij, “Credit Card Fraud Detection using Machine Learning, ” 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), MORADABAD, India, 2021, pp. 167-171. doi:10.1109/SMART52563.2021.9676262 [Google Scholar]
  14. Vynokurova O, Peleshko D, Bondarenko O, Ilyasov V, Serzhantov V, Peleshko M. Hybrid machine learning system for solving fraud detection tasks. In: 2020 IEEE third international conference on data stream mining & processing (DSMP), IEEE; 2020. p. 1–5. [Google Scholar]
  15. Rai AK, Dwivedi RK. Fraud detection in credit card data usingunsupervised machine learning based scheme. In: IEEE, 2020. p. 421–426. [Google Scholar]
  16. Dubey SC, Mundhe KS, Kadam AA. Credit card fraud detection using artificial neural network and back propagation. In: 2020 4th international conference on intelligent computing and control systems (ICICCS). IEEE; 2020. p. 268–273. [Google Scholar]
  17. Patidar R, Sharma L. Credit card fraud detection using neuranetwork. Int J Soft Comput Eng (IJSCE), 2011;1(32–38). [Google Scholar]
  18. Dhankhad S, Mohammed E, Far B. Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In: IEEE, 2018. p. 122–125. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.