Open Access
Issue
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
Article Number 01022
Number of page(s) 6
Section Traffic Prediction and Analysis
DOI https://doi.org/10.1051/itmconf/20257001022
Published online 23 January 2025
  1. Techopedia, Credit Card Fraud Statistics, https://www.techopedia.com/credit-card-fraud-statistics (2024), Accessed time: September 4, 2024. [Google Scholar]
  2. P. Chatterjee, D. Das, D. Rawat, Securing financial transactions: Exploring the role of federated learning and blockchain in credit card fraud detection. Authorea Preprints (2023). [Google Scholar]
  3. Y. Lu, et al., WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet. Applied Soft Computing 101: 107006 (2021). [CrossRef] [Google Scholar]
  4. R. Xia, S. Kais, Quantum machine learning for electronic structure calculations. Nature Communications 9.1, 4195 (2018). [CrossRef] [Google Scholar]
  5. R. Hafezi, J. Shahrabi, E. Hadavandi, A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price. Applied Soft Computing 29, 196-210 (2015). [CrossRef] [Google Scholar]
  6. Y. Sahin, E. Duman, Detecting credit card fraud by decision trees and support vector machines. Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1 (2011). [Google Scholar]
  7. Y. Sahin, S. Bulkan, E. Duman, A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications 40. 15, 5916-5923 (2013). [CrossRef] [Google Scholar]
  8. M. A. Salam, et al., Federated learning model for credit card fraud detection with data balancing techniques. Neural Computing and Applications 36.11, 6231–6256 (2024). [CrossRef] [Google Scholar]
  9. M. Li, J. Walsh, FedGAT-DCNN: Advanced Credit Card Fraud Detection Using Federated Learning, Graph Attention Networks, and Dilated Convolutions. Electronics 13.16: 3169 (2024). [CrossRef] [Google Scholar]
  10. B. McMahan, et al., Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics. PMLR (2017). [Google Scholar]
  11. W. Yang, et al., FFD: A federated learning-based method for credit card fraud detection. Big Data-BigData 2019: 8th International Congress, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25-30, 2019. Springer International Publishing (2019). [Google Scholar]
  12. K. Bian, H. Zheng, FedAvg-DWA: A novel algorithm for enhanced fraud detection in federated learning environment. 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE (2023). [Google Scholar]
  13. L. Wang, et al., Addressing class imbalance in federated learning. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, No. 11 (2021). [Google Scholar]
  14. A. Reisizadeh, et al., Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity. IEEE Journal on Selected Areas in Information Theory 3.2: 197–205 (2022). [CrossRef] [Google Scholar]
  15. O. Shahid, et al., Communication efficiency in federated learning: Achievements and challenges. arXiv preprint arXiv:2107.10996 (2021). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.