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.
| Published in | Mathematics and Computer Science (Volume 3, Issue 2) |
| DOI | 10.11648/j.mcs.20180302.11 |
| Page(s) | 46-53 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
SOFM, Clustering Analysis, Transaction State Detection
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APA Style
Xin Chen, Weidong Tian, Wenyuan Sun. (2018). Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model. Mathematics and Computer Science, 3(2), 46-53. https://doi.org/10.11648/j.mcs.20180302.11
ACS Style
Xin Chen; Weidong Tian; Wenyuan Sun. Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model. Math. Comput. Sci. 2018, 3(2), 46-53. doi: 10.11648/j.mcs.20180302.11
AMA Style
Xin Chen, Weidong Tian, Wenyuan Sun. Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model. Math Comput Sci. 2018;3(2):46-53. doi: 10.11648/j.mcs.20180302.11
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Download@article{10.11648/j.mcs.20180302.11,
author = {Xin Chen and Weidong Tian and Wenyuan Sun},
title = {Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model},
journal = {Mathematics and Computer Science},
volume = {3},
number = {2},
pages = {46-53},
doi = {10.11648/j.mcs.20180302.11},
url = {https://doi.org/10.11648/j.mcs.20180302.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20180302.11},
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.},
year = {2018}
}
TY - JOUR T1 - Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model AU - Xin Chen AU - Weidong Tian AU - Wenyuan Sun Y1 - 2018/04/09 PY - 2018 N1 - https://doi.org/10.11648/j.mcs.20180302.11 DO - 10.11648/j.mcs.20180302.11 T2 - Mathematics and Computer Science JF - Mathematics and Computer Science JO - Mathematics and Computer Science SP - 46 EP - 53 PB - Science Publishing Group SN - 2575-6028 UR - https://doi.org/10.11648/j.mcs.20180302.11 AB - 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. VL - 3 IS - 2 ER -
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