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Optimization methods applied to neural networks for anomaly detection in credit card transactions

Grant number: 19/13420-4
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): September 01, 2019
Effective date (End): August 31, 2020
Field of knowledge:Physical Sciences and Mathematics - Mathematics - Applied Mathematics
Principal Investigator:Luis Felipe Cesar da Rocha Bueno
Grantee:Frederico José Ribeiro Pelogia
Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil
Associated research grant:18/24293-0 - Computational methods in optimization, AP.TEM


In this project, it is intended to study optimization methods, especially stochastic algorithms, applied to the training of neural networks. In a recent paper proposed by BUENO, L. F. and MARTÍNES,J. M., "On the complexity of solving feasibility problems", 2018, a first-order algorithm was presented with good complexity results for least squares problems. One of this project's research main point is to develop a stochastic version of this algorithm. The performance of the studied algorithms will be analyzed when applied to fraud detection in credit card operations using Kaggle's Credit Card Fraud Detection database.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BORTOLETE, J. C.; BUENO, L. F.; BUTKERAITES, R.; CHAVES, A. A.; COLLACO, G.; MAGUETA, M.; PELOGIA, F. J. R.; SALLES NETO, L. L.; SANTOS, T. S.; SILVA, T. S.; et al. A support tool for planning classrooms considering social distancing between students. COMPUTATIONAL & APPLIED MATHEMATICS, v. 41, n. 1, . (16/01860-1, 19/13420-4, 18/24293-0, 13/07375-0)

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