A Novel Approach Integrating Autoencoders and ESMOTE-GAN for Credit Card Fraud Detection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61929-8_2
Reference14 articles.
1. Ghaleb, F.A., Saeed, F., Al-Sarem, M., Qasem, S.N., Al-Hadhrami, T.: Ensemble synthesized minority oversampling- based generative adversarial networks and random forest algorithm for credit card fraud detection. IEEE Access 11, 89694–89710 (2023). https://doi.org/10.1109/access.2023.3306621
2. Dandotia, S., Tiwari, S.K.: Detection of credit card fraud transactions using machine learning algorithms techniques with data driven approaches: a comparative study. Int. J. Innov. Res. Growth 10(11), 101 (2021). https://doi.org/10.26671/ijirg.2021.11.10.101
3. Vishal Kumar, V.: Credit card fraud detection using machine learning. Comput. Intell. Mach. Learn. 4(1), 39 (2023). https://doi.org/10.36647/ciml/04.01.a009
4. Ileberi, E., Sun, Y., Wang, Z.: A machine learning based credit card fraud detection using the GA algorithm for feature selection. J. Big Data 9, 24 (2022). https://doi.org/10.1186/s40537-022-00573-8
5. Singh, S., Chopra, V.: Hybrid machine learning algorithm for credit card fraud detection. Int. Res. J. Modern. Eng. Technol. Sci. 4, 134 (2022). https://doi.org/10.56726/irjmets29605
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