An integration of deep learning model with Navo Minority Over-Sampling Technique to detect the frauds in credit cards

Author:

Karthika J.,Senthilselvi A.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference32 articles.

1. Altiti O (2020) Credit card fraud detection based on machine and deep learning. In: 2020 11th International Conference on Information and Communication Systems (ICICS), pp 204–208, Irbid, Jordan

2. Asha RB, KR SK, (2021) Credit card fraud detection using artificial neural network. Global Transitions Proceedings 2(1):35–41

3. Benchaji I, Douzi S, Ouahidi BE (2019) Using genetic algorithm to improve classification of imbalanced datasets for credit card fraud detection. Lect Notes Netw Syst 66:220–229

4. Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Data mining for credit card fraud: a comparative study. Decis Support Syst 50(3):602–613

5. Boracchi G, Caelen O, Alippi C, Dal Pozzolo A (2017) Credit card fraud detection: a realistic modeling a novel learning strategy. IEEE Trans Neural Netw Learn Syst 2162-237X

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1. Garra Rufa Fish Optimization-based K-Nearest Neighbor for Credit Card Fraud Detection;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15

2. Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions;IEEE Access;2024

3. Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness;2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES);2023-12-14

4. CTCN: a novel credit card fraud detection method based on Conditional Tabular Generative Adversarial Networks and Temporal Convolutional Network;PeerJ Computer Science;2023-10-10

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