AI-Driven Weighted and Aggregated LSTM Model for Enhanced Credit Card Usage Monitoring and Suspect Transaction Identification

Authors

  • Rini Angeline Vinisha J, M.Jeslin Benita Ponnarasi, Mithuna R, M.Sakthivadivel, Dr. P.Vivekanandan, S. Arunbalaji

Keywords:

Sequential Memory Network, LOSTM Memory Cells, Activation Functions

Abstract

The type  of Sequential Memory Network popularly known to be  the LOSTM (Long Short-Term Memory) model is created to overcome the existing problems  of conventional Sequential Memory Networks in capturing long-term relationships in sequential input. It is frequently used in the domains of  speech recognition, time series analysis, and natural language processing, but we employ it for payment card usage detection and activity monitoring for questionable transactions. Memory Cells, Gates , Cell State, Hidden State, Activation Functions, and Back propagation Through Time are just a few of the essential elements and processes that make up an LOSTM model. The proposed model  offer a weighted and aggregated model with a change to the standard LOSTM model to provide better accuracy than the existing standard LOSTM model. The performance of the model is compared with GRU 2020, SVM(2021) ,KNN(2021) and ANN(2021) using accuracy, precision and recall parameters.

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Published

2025-04-25

How to Cite

1.
Rini Angeline Vinisha J, M.Jeslin Benita Ponnarasi, Mithuna R, M.Sakthivadivel, Dr. P.Vivekanandan, S. Arunbalaji. AI-Driven Weighted and Aggregated LSTM Model for Enhanced Credit Card Usage Monitoring and Suspect Transaction Identification. J Neonatal Surg [Internet]. 2025Apr.25 [cited 2025Oct.12];14(18S):268-73. Available from: https://jneonatalsurg.com/index.php/jns/article/view/4604

Issue

Vol. 14 No. 18S (2025): Journal of Neonatal Surgery

Section

Original Article

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