Volume 3, Issue 2, March 2018, Page: 46-53
Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model
Xin Chen, Mathematics Department, Yanbian University, Yanji, P. R. China
Weidong Tian, Mathematics Department, Yanbian University, Yanji, P. R. China
Wenyuan Sun, Mathematics Department, Yanbian University, Yanji, P. R. China
Received: Jan. 26, 2018;       Accepted: Mar. 14, 2018;       Published: Apr. 9, 2018
DOI: 10.11648/j.mcs.20180302.11      View  1319      Downloads  84
Abstract
In order to solve these problems such as monitoring the ATM behavior of operation, exception detection for ATM transaction status and so on, in this paper we establish the detecting system of SOFM for the ATM to raise the timely alarm and reduce the false alarm rate. The results of SOFM model simulation show that the ATM transaction exceptions collected in data base can be timely and accurately detected and the false alarm rate is low. The model has high classification accuracy, which verifies its effectiveness.
Keywords
SOFM, Clustering Analysis, Transaction State Detection
To cite this article
Xin Chen, Weidong Tian, Wenyuan Sun, Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model, Mathematics and Computer Science. Vol. 3, No. 2, 2018, pp. 46-53. doi: 10.11648/j.mcs.20180302.11
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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