A Multi-layered Hidden Markov Model for Real-Time Fraud Detection in Electronic Financial Transactions | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 4، بهمن 2023، صفحه 599-608 اصل مقاله (1.86 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2023.11990.2357 | ||
| نویسندگان | ||
| Abdul Aziz Danaa Abukari* 1؛ Mohammed Daabo Ibrahim2؛ Alhassan Abdul-Barik3 | ||
| 1Department of Computer Science, Tamale Technical University, Tamale, Ghana. | ||
| 2Department of Computer Science, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana. | ||
| 3Department of Computer Science, University for Development Studies, Tamale, Ghana. | ||
| چکیده | ||
| Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing drastically the number of false positives and negatives is proposed and implemented in this study. The study also focused on reducing the parameter optimization and detection times of the proposed models using a hybrid algorithm comprising the Baum-Welch, Genetic and Particle-Swarm Optimization algorithms. Simulation results revealed that, in terms of Precision, Recall and F1-scores, our proposed model performed better when compared to other approaches proposed in literature. | ||
| کلیدواژهها | ||
| Fraudulent, Hidden Markov Models, Optimization؛ Probability؛ Multi-Layered | ||
| مراجع | ||
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