شماره ركورد كنفرانس :
5134
عنوان مقاله :
Credit Card Fraud Detection by Combining Neural Network and Grasshopper Optimization Algorithm
پديدآورندگان :
Fadhil Smaisim Ghassan Nanotechnology and Advanced Materials Research Unit (NAMRU) University of Kufa, Kufa, Iraq , Houshmand Mohammad Faculty of Electrical Department, Imam Reza International University, Mashhad, Iran , Alobayes Falah mahdi Faculty of Electrical Department, Imam Reza International University, Mashhad, Iran , Alkhafaji Alaa Mahdi Faculty of Electrical Department, Imam Reza International University, Mashhad, Iran
كليدواژه :
Fraud Detection , Grasshopper Optimization Algorithm , Neural Network
عنوان كنفرانس :
دومين كنفرانس بين المللي محاسبات و سامانه هاي توزيع شده
چكيده فارسي :
With the accelerated development of Internet finance, electronic funds transfer and the rapid growth of credit card activity, credit cards play a very important role in every area of life today. There are some risks in this regard that are considered serious threats to both issuers and cardholders. With the increasing number of fraudulent credit card transactions, forged credit cards and fraudulent use of expired credit cards have led to increased losses. Therefore, finding fraud detection techniques accurately and quickly has become an important topic in current investigations. In this study, after normalizing and reducing the dimensionality of the data using the PCA algorithm, we used the modified perceptron neural network and the grasshopper algorithm to classify the data. In this study, we use the grasshopper algorithm to adjust the weights and biases of neural networks. In the end, we were able to achieve 99.20% accuracy.
