Accuracy Analysis of Data Fraud Detection for Company Transactions Using Two-Layered Feed Forward Neural Network Approach Compared with Random Forest
Aim: To find accuracy of data fraud detection for company transactions using two-layered feed forward Neural Network approaches compared with Random Forest. Materials and methods: Number of groups is 2. Group 1 - Two-layered feed forward Neural Network, and Group 2 - Random Forest. Results: The accu...
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Veröffentlicht in: | ECS transactions 2022-04, Vol.107 (1), p.13481-13490 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | Aim: To find accuracy of data fraud detection for company transactions using two-layered feed forward Neural Network approaches compared with Random Forest. Materials and methods: Number of groups is 2. Group 1 - Two-layered feed forward Neural Network, and Group 2 - Random Forest. Results: The accuracy of the innovative feed forward Neural Network method is 83.4% and Random Forest is 85.5%. The accuracy of the Random Forest algorithm appears to be a slight increase. Conclusion: Random Forest algorithm finds the fraud detection appearance to be better accuracy than the feed forward Neural Network. |
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ISSN: | 1938-5862 1938-6737 |
DOI: | 10.1149/10701.13481ecst |