TAX EVASION DETECTION BY USING COMBINATORY INTELLIGENT SYSTEM

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

RAHIMIKIA EGHBAL; MOHAMADI SHAPOUR; GHAZANFARI MEHDI

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Tax Research Year:2015 | Volume:23 | Issue:26 (74) Page(s): 135-163

Download Full-Text

Persian Verion

View:

2,034

Download:

Cites:

Information Journal Paper

Title

TAX EVASION DETECTION BY USING COMBINATORY INTELLIGENT SYSTEM

Author(s)

RAHIMIKIA EGHBAL | MOHAMADI SHAPOUR | GHAZANFARI MEHDI | Issue Writer Certificate

Keywords

TAX EVASION DETECTIONQ2

DATA MININGQ2

ARTIFICIAL INTELLIGENCEQ1

COMBINATORY INTELLIGENT SYSTEMQ2

Abstract

With the electronic taxpayers system being operationalized and the digital storage of tax data developed in Iran, it is now possible to design different models to analyze the available data. There are two main areas that have not been the focus of the fairly limited current studies in this field; one being the parallel optimization of parametric AI models and the other area is the selection of input variable combination. For this reason, in present study, we have used the harmony search (HS) optimization algorithm to do parallel optimization of multilayer perceptron (MLP) neural network parameters and also to find a suitable combination of input variables. In addition to that, the results have been compared with logistic regression results as the core of the system. In the present research, 21 initial input variables are selected for the system based on the survey done on similar studies in the last thirty years and it takes into account the specifications of the tax system in Iran and the opinions of the experts in the field are asked. After running the system on the data from the food and textile sectors and comparing the results from the neural network and logistic regression, we have concluded that neural network can produce more accurate results and the difference is statistically meaningful.

Cites

No record.

References

No record.

Cite

APA: Copy

RAHIMIKIA, EGHBAL, MOHAMADI, SHAPOUR, & GHAZANFARI, MEHDI. (2015). TAX EVASION DETECTION BY USING COMBINATORY INTELLIGENT SYSTEM. TAX JOURNAL, 23(26 (74)), 135-163. SID. https://sid.ir/paper/89700/en

Vancouver: Copy

RAHIMIKIA EGHBAL, MOHAMADI SHAPOUR, GHAZANFARI MEHDI. TAX EVASION DETECTION BY USING COMBINATORY INTELLIGENT SYSTEM. TAX JOURNAL[Internet]. 2015;23(26 (74)):135-163. Available from: https://sid.ir/paper/89700/en

IEEE: Copy

EGHBAL RAHIMIKIA, SHAPOUR MOHAMADI, and MEHDI GHAZANFARI, “TAX EVASION DETECTION BY USING COMBINATORY INTELLIGENT SYSTEM,” TAX JOURNAL, vol. 23, no. 26 (74), pp. 135–163, 2015, [Online]. Available: https://sid.ir/paper/89700/en

Related Journal Papers