https://orcid.org/0000-0002-9020-3885
References
- Aggarwal, C. C. 2014. Data classification: Algorithms and applications. Boca Raton, Florida, United States: CRC press.
- Banjanovic-Mehmedovic, L., A. Hajdarevic, M. Kantardzic, F. Mehmedovic, I. Dzananovic. 2017. Neural network-based data-driven modelling of anomaly detection in thermal power plant. Automatika. 58(1):69–79. doi:10.1080/00051144.2017.1343328.
- Chouiekh, A., and I. E. L. H. EL Hassane. 2018. Convnets for fraud detection analysis. Procedia Computer Science 127:133–38. doi:10.1016/j.procs.2018.01.107.
- Hastie, T., Tibshirani, R., Friedman, J., and Franklin, J. 2005. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer 27(2):83-85. Springer. doi:10.1007/BF02985802.
- Haykin, S. 1994. Neural networks: A comprehensive foundation. Upper Saddle River, New Jersey, United States: Prentice Hall PTR.
- Kelleher, J. D., B. M. Namee, and A. D’arcy. 2015. Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies. Cambridge, Massachusetts, United States: MIT Press.
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12 Oct: 2825–30.
- Qi, L., and J. Sun. 1993. A nonsmooth version of Newton’s method. Mathematical Programming 58 (1–3):353–67. doi:10.1007/BF01581275.
- Taboada-Crispi, A., Sahli, H., Hernandez-Pacheco, D., Falcon-Ruiz, A. 2009. Anomaly detection in medical image analysis. In Handbook of research on advanced techniques in diagnostic imaging and biomedical applications, 426–46. Pennsylvania, United States: IGI Global.
- Zanin, M., M. Romance, S. Moral, R. Criado, et al. 2018. Credit card fraud detection through parenclitic network analysis. Complexity 2018. Hindawi. doi:10.1155/2018/5764370.