Title: Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection
Authors: Annu Paul; Varghese Paul
Addresses: Department of Computer Science, Alphonsa College, Pala, Kottayam, Kerala, India ' Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi, Kerala, India
Abstract: This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation phase, transforming data using Yeo-Johnson (YJ) transformation. Then, the feature selection procedure is done by the Fisher score for creating the unique and significant features. Next, based on the selected textures, the data augmentation mechanism is done using the oversampling model. At last, the fraud detection is carried out by the Deep Maxout network, which is trained by the proposed PRSA optimisation algorithm, derived by integrating Poor and Rich Optimisation (PRO) and Squirrel Search Algorithm (SSA). The integration of parametric features of the PRSA algorithm effectively trained the classifier to update weights to generate the best solution by considering fitness measures. The proposed method achieved the best accuracy, sensitivity, and specificity measures of 0.96, 0.95 and 0.94, respectively.
Keywords: credit card; deep learning; fraud detection; data augmentation; data transformation.
DOI: 10.1504/IJWMC.2025.144181
International Journal of Wireless and Mobile Computing, 2025 Vol.28 No.2, pp.123 - 134
Received: 20 Aug 2021
Accepted: 08 Mar 2022
Published online: 31 Jan 2025 *