Deep learning (DL), a branch of machine learning (ML), is the core technology in today's
technological advancements and innovations. Deep learning-based approaches are the …

Fraud detection in banking systems is crucial for financial stability, customer protection,
reputation management, and regulatory compliance. Machine Learning (ML) is vital in …

This study addresses the critical challenge of data imbalance in credit card fraud detection
(CCFD), a significant impediment to accurate and reliable fraud prediction models. Fraud …

Accurate diagnosis of breast cancer using automated algorithms continues to be a
challenge in the literature. Although researchers have conducted a great deal of work to …

Imbalanced learning jeopardizes the accuracy of traditional classification models,
particularly for what concerns the minority class, which is often the class of interest. This …

The increasing use of credit cards in global financial transactions offers significant
convenience for consumers and businesses. However, credit card fraud remains a major …

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting
fraudulent transactions is challenging due to the imbalance in class distribution, where the …

Sažetak A significant research challenge in data mining and machine learning is class
imbalance classification since the majority of real-world datasets are imbalanced. When the …

A novel deep learning-based hybrid Harris hawks with sine cosine approach for credit card
fraud detection Page 1 AIMS Mathematics, 8(10): 23200–23217. DOI: 10.3934/math.20231180 …

The proliferation of digital payment methods facilitated by various online platforms and
applications has led to a surge in financial fraud, particularly in credit card transactions …