@article{Taha2023, 
author = {Altyeb Taha},
title = {A novel deep learning-based hybrid Harris hawks with sine cosine approach for credit card fraud detection},
year = {2023},
journal = {AIMS Mathematics},
volume = {8},
number = {10},
pages = {23200-23217},
keywords = {neural network, deep learning, hyperparameter optimization, features selection, fraud detection, credit card},
url = {https://www.sciopen.com/article/10.3934/math.20231180},
doi = {10.3934/math.20231180},
abstract = {Credit cards have become an integral part of the modern financial landscape, and their use is essential for individuals and businesses. This has resulted in a significant increase in their usage in recent years, especially with the growing popularity of online payments. Unfortunately, this increase in credit card use has also led to a corresponding rise in credit card fraud, posing a serious threat to financial security and privacy. Therefore, this research introduces a novel deep learning-based hybrid Harris hawks with sine cosine method for credit card fraud detection system (HASC-DLCCFD). The aim of the presented HASC-DLCCFD approach is to identify fraudulent credit card transactions. The suggested HASC-DLCCFD scheme introduces a HASC technique for feature selection, by combining Harris hawks optimization (HHO) with the sine cosine algorithm (SCA). For the purpose of identifying credit card fraud, an architecture of a convolutional neural network combined with long short-term memory (CNN–LSTM) is utilized in this study. Finally, the adaptive moment estimation (Adam) algorithm is utilized as a hyperparameter optimizer of the CNN-LSTM model. The performance of the suggested HASC-DLCCFD approach was experimentally evaluated using a publicly available database. The results demonstrate that the suggested HASC-DLCCFD approach outperforms other current techniques and achieved the highest accuracy of 99.5%.}
}