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It is a serious problem of tax evasion in all domains. Because of the narrow human factors, there are case-by-case basis not to examine reported tax cases. Hence, it is a very demanding challenge to grow an effective tax evasion detection mechanism. In this paper, we use the proposed rule extractor approaches (PFHRCNN) to detect tax evasion. The experiment results show the proposed PFHRCNN for tax evasion detection systems is a good way.
|Title of host publication||ICCST 2015 - The 49th Annual IEEE International Carnahan Conference on Security Technology|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - 21 Jan 2016|
|Event||49th Annual IEEE International Carnahan Conference on Security Technology, ICCST 2015 - Taipei, Taiwan|
Duration: 21 Sep 2015 → 24 Sep 2015
|Name||Proceedings - International Carnahan Conference on Security Technology|
|Conference||49th Annual IEEE International Carnahan Conference on Security Technology, ICCST 2015|
|Period||21/09/15 → 24/09/15|
- fuzzy neural network
- particle swarm optimization
- tax evasion
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