[1] Maryam Habibpour Hassan Gharoun, Mohammadreza Mehdipour, AmirReza Tajally, etc., “Uncertainty-Aware Credit
Card Fraud Detection Using Deep Learning Joao” P.S.Catalao, https://arxiv.org/abs/2107.13508;2021
[2] P. Shenvi, N. Samant, S. Kumar, and V. Kulkarni, “Credit Card Fraud Detection using Deep Learning”,2019 IEEE
5th International Conference for Convergence in Technology (I2CT), Bombay, India,2019, pp.1-5,
doi:10.1109/I2CT45611.2019.9033906.
[3] Xiaoli Shen, Vedant Jain, and Xin Huang “Detect fraudulent transactions using machine learning with Amazon Sage
Maker” Amazon Sage Maker blog, Jump Start, Artificial Intelligence, OCT2022.
[4] R.S.More, C.J.Awati, et al., “Credit Card Fraud Detection Using Supervised Learning Approach”, International Journal of
Scientific & Technology Research, volume 9, issue 10, October 2020, pp. 216–218, ISSN 2277-86162.
[5] Géron, “A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and
Techniques to Build Intelligent Systems”, O’ReillyMedia, 2019.
[6] F. Chollet, “Deep Learning with Python”, Manning Publications, 2018.
[7] Amazon Sage Maker Documentation.(https://docs.aws.amazon.com/sagemaker/)
[8] Amazon Web Services.(https://aws.amazon.com/)
[9] M.M.Breunig, et al., “LOF: Identifying density-based local outliers”,ACMSIGMOD Record,29(2),93-104,2000.
[10] C.M. Bishop,” Pattern Recognition and Machine Learning”, Springer, 2006.
[11] J. Davis and M. Goodrich, “The relationship between Precision-Recall and ROC curves”, In Proceedings of the 23rd
International Conference on Machine Learning (ICML'06),233-240,2006.
[12] He, H.,&Wu, D.(2019), “Transfer learning for neural networks: A Survey”, IEEE Transactions on Neural Networks and
Learning Systems, 30(5),1359-1379.
[13] Kingma, D. P., & Ba, J. (2014). Adam: “A method for stochastic optimization.” ArXivpreprintar Xiv:1412.6980.
[14] LeCun, Y., Bengio, Y., & Hinton, G.(2015), “Deep learning. Nature”, 521(7553), 436-444.
[15] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., &Vanderplas, J. “Scikit-learn:
Machine learning in Python” Journal of Machine Learning Research, 2011, pp. 2825-2830.
[16] Rendle, S, Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L.(2009).BPR: Bayesian personalized ranking from
implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI'09),
452-461.
[17] Sage Maker Random Cut Forest(RCF) Algorithm
Documentation.(https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutfore st.html)
[18] Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola,A.J.,&Williamson,R.C.(2001).Estimating the Support of a HighDimensional Distribution. Neural Computation, 13(7), 1443-1471.
[19] Theis, F. J., &Bethge, M. (2015). “Generative image modeling using spatial LSTMs. In Advances in Neural
Information Processing Systems (NIPS'15)”,1927-1935.
[20] Zheng, Z., Zheng, L., & Yang, Y.(2017). “Unlabeled samples generated by GAN improve the person re-identification
baseline in vitro.” In Proceedings of the IEEE International Conference on Computer Vision (ICCV'17),3754-3762.
[21] Rafael San Miguel Carrasco And Miguel-Ángelsicilia-Urbán2, “Evaluation of Deep Neural Networks for Reduction of
Credit Card Fraud Alerts”, pages 186421- 186432, 2020, IEEEAccessDOI10.1109/ACCESS.2020.3026222.
[22] “Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon Sage Maker and Amazon” ©
2019 John Wiley & Sons, Inc. Published 2019 by John Wiley & Sons, Inc.
[23] Y. Kou, C.-T. Lu, S. Sirwongwattana, and Y.-P. Huang, “Survey of fraud detection techniques,” in IEEE International Conference on Networking, Sensing and Control,2004,vol.2.IEEE,2004, pp.749-754
[24]https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html Learning Boto3S3 and
documentation.
[25] Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, CesareAlippi, and Gianluca Botempi, “Credit card Fraud
Detection: A realistic Modeling and a Novel Learning Strategy,” IEEE Trans. On Neural Network and Learning
System, August 2018, vol. 29, No.8.
[26] Amazon Sage Make rhttps://d1.awsstatic.com/whitepapers/julia-onsagemaker.pdf?did=wp_card&trk=wp_card
[27] Vinod Jain, Mayank Agrawal, Anuj Kumar, “Performance Analysis of Machine Learning Algorithms in Credit Cards
Fraud Detection",2020 8
th International Conference on Reliability, Infocom Technologies and Optimization(Trends
and Future Directions) (ICRITO), Amity University, June 4-5,2020, Noida, India.
[28] Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, YacineKessaci, FrédéricOblé, Gianluca Bontempi,
“Combining unsupervised and supervised learning in credit card fraud detection, Information Sciences”, 2019,
ISSN0020- 0255,https://doi.org/10.1016/j.ins.2019.05.042.ARMEL and D.ZAIDOUNI,
[29] "Fraud Detection Using Apache Spark," 2019 5th International Conference on Optimization and Applications
(ICOA), Kenitra, Morocco,2019, pp.1- 6.doi:10.1109/ICOA.2019.87
