چکیده مقاله
This article proposes suitable solution to detect fraud via fuzzy logic followed by Neuralfuzzy Takagi Sugeno training method In order for the fraud to be detected through fuzzylogic, there should be some rules stemmed from experience of the experts These rules are expressed through information that could be registered for a given card To come up with thefuzzy deduction, membership functions needed to be expressed over the specified input range This issue is one of the problems of fuzzy logic To solve this problem, fuzzy logics were established and Mamdani deduction engines were utilized as a result of which suitable responses were presented for fraud detection via Neural fuzzy method Despite the fact thatthe problem inputs were highly linear, Neural fuzzy training was able to cope with the problem and present a suitable trained system In other words, Neural fuzzy training method is employed in order to optimize the fuzzy logic membership functions based on the data Outcomes of the Neural fuzzy training were quite satisfactory and highly precise Thus, utilizing the research findings, Neural fuzzy training method is proposed for upgrading fraud detection in the banking system of our country
کلیدواژهها
نویسندگان
Fatemeh Shafiee Nezhad
Amirkabir University of Technology, Tehran, Iran
Hamid Reza Shahriari
Fatemeh Shafiee Nezhad Amirkabir University of Technology, Tehran, Iran
شیوه ارجاع
Shafiee Nezhad, Fatemeh and Shahriari, Hamid Reza,1392,Fuzzy logic and Takagi-Sugeno Neural-Fuzzy to Deutsche Bank Fraud Transactions,7th International Conference on e- Commerce whit focus on e-Security,Kish Island
ارائهشده در
مجموعه مقالات هشتمین کنفرانس بین المللی تجارت الکترونیک با رویکرد بر اعتماد الکترونیکی4 اردیبهشت 1393 · مشهد