| Peer-Reviewed

Exception Detection for ATM Transaction Status Based on a Self-Organizing Feature Mapping Model

Received: 26 January 2018     Accepted: 14 March 2018     Published: 9 April 2018
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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.

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

Keywords

SOFM, Clustering Analysis, Transaction State Detection

References
[1] Qian Wang. Analysis and research of ATM transaction status. Beijing: school of mathematics, north China electric power university, 2017. 07. 28.
[2] Ding Lin, Yufeng Li, Shuaiwei Yuan, Zhi Zheng. Changchun: journal of jilin university of architecture, Vol. 34 No. 6 Dec. 2017.
[3] Simon D. Biogeography-based optimization [J]. IEEE Transaction on Evolutionary Computation, 2008, 12 (6): 702-713.
[4] Jian Lin, Li Xiu. Estimation of chaotic system parameters based on hybrid biogeographic optimization [J]. Journal of physics, 2013, 62 (3): 1-7.
[5] Boussad I Chatterjee A, Siarry P. Two-stage update biogeography-based optimization using differential evolution alorithem (DBBO) [J]. Computers and Operations Research, 2011, 38 (8): 1188-1198.
[6] Zhizheng Du. Research on the efficiency model of BP neural network electric dusting [J]. Northeast electric power technology, 2014, 34 (9): 29-34.
[7] XiaoXue Wang, LinShan Wang. Comprehensive evaluation of students based on SOFM neural network [J]. Journal of hebei normal university (natural science edition), 2011, 35 (3): 239-243.
[8] KOHONEN T. Self-Organization and Associative Memory [M]. 3rd. New York: Spring Verleg, 1989.
[9] Gu baoping, Guo hongyan. Based on dynamic SOFM network intrusion detection [J]. Computer security, 22-24, 2009. 8.
[10] Liping Mo. Fault diagnosis method based on Kohonen neural network [J]. Journal of chengdu university: natural science edition, 2007, 26 (1): 1249-1251, 1275.
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[12] Li Qing, Shengyue Zhang, Yongsheng Yang, Zhiqiang Lian. Analysis of dust source in lead-zinc mine based on SOFM neural network. KunMing: Kunming University of Science and Technology College of Land and Resources Engineering, 2016, 7(37).
[13] Ni buxi. A cluster analysis based on SOFM network [J]. Computer engineering and design, 2006. 3, 5(27):855-856.
[14] Shi Feng. Intelligent Algorithm in MATLAB-30 Case Analysis [M]. Beijing:Beihang University Press, 2011.
Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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  • 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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Author Information
  • Mathematics Department, Yanbian University, Yanji, P. R. China

  • Mathematics Department, Yanbian University, Yanji, P. R. China

  • Mathematics Department, Yanbian University, Yanji, P. R. China

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