Open Access
| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 03005 | |
| Number of page(s) | 4 | |
| Section | Digital Development and Environmental Management of Energy Supply Chain | |
| DOI | https://doi.org/10.1051/e3sconf/202021403005 | |
| Published online | 07 December 2020 | |
- Awoyemi, John O., Adebayo O.Adetunmbi., and Samuel A.Oluwadare.. “Credit card fraud detection using machine learning techniques: A comparative analysis. ” 2017 International Conference on Computing Networking and Informatics (ICCNI). IEEE, 2017. [Google Scholar]
- Yee O.S., Sagadevan S., & Malim, N.H. A. H. (2018). Credit card fraud detection using machine learning as data mining technique. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-4), 23-27. [Google Scholar]
- Dhankhad S., Mohammed E., & Far B. (2018, July). Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. In 2018 IEEE International Conference on Information Reuse and Integration (IRI) (pp. 122-125). IEEE. [Google Scholar]
- Beale, H.D., Demuth, H.B., & Hagan, M.T. (1996). Neural network design. Pws, Boston. [Google Scholar]
- Hecht-Nielsen R. (1992). Theory of the backpropagation neural network. In Neural networks for perception (pp. 65-93). Academic Press. [CrossRef] [Google Scholar]
- Tolles J., & Meurer, W.J. (2016). Logistic regression: relating patient characteristics to outcomes. Jama, 316(5), 533-534. [Google Scholar]
- Murphy, K.P. (2006). Naive bayes classifiers. University of British Columbia, 18, 60. [Google Scholar]
- https://www.kaggle.com/c/ieee-fraud-detection/data [Google Scholar]
- Krizhevsky A., Sutskever I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). [Google Scholar]
- Socher R., Bengio Y., & Manning, C.D. (2012, July). Deep learning for NLP (without magic). In Tutorial Abstracts of ACL 2012 (pp. 5-5). Association for Computational Linguistics. [Google Scholar]
- Deng L., & Platt, J.C. (2014). Ensemble deep learning for speech recognition. In Fifteenth Annual Conference of the International Speech Communication Association. [Google Scholar]
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