Digital transactions relying on credit cards are gradually improving in recent days due to their convenience. Due to the tremendous growth of e-services (e.g., mobile payments, e-commerce, and e-finance) and the promotion of credit cards, fraudulent transaction counts are rapidly increasing. Machine learning (ML) is crucial in investigating customer data for detecting and preventing fraud. Conversely, the advent of irrelevant and redundant features in most real-time credit card details reduces the execution of ML techniques. The feature selection (FS) approach's purpose is to detect the most prominent attributes required for developing an effective ML approach, making sure that the classification and computational complexity are improved and decreased, respectively. Therefore, this study presents an evolutionary computing with fuzzy autoencoder based data analytics for credit card fraud detection (ECFAE-CCFD) technique. The purpose of the ECFAE-CCFD technique is to recognize the presence of credit card fraud (CCF) in real time. To achieve this, the ECFAE-CCFD technique performs data normalization in the earlier stage. For selecting features, the ECFAE-CCFD technique applies the dandelion optimization-based feature selection (DO-FS) technique. Moreover, the fuzzy autoencoder (FAE) approach can be exploited for the recognition and classification of CCF. FAE is a category of artificial neural network (ANN) designed for unsupervised learning that leverages fuzzy logic (FL) principles to enhance the representation and reconstruction of input data. An improved billiard optimization algorithm (IBOA) could be implemented for the optimum selection of the parameters based on the FAE algorithm to improve the classification performance. The simulation outcomes of the ECFAE-CCFD algorithm are examined on the benchmark open-access database. The values display the excellent performance of the ECFAE-CCFD method with respect to various measures.

Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early classification of MRI (Magnetic Resonance Imaging) images in a multi-class and binary-class MS dataset. All hybrid systems started by enhancing MRI images using the fusion processes of a Gaussian filter and Contrast-Limited Adaptive Histogram Equalization (CLAHE). Then, the Gradient Vector Flow (GVF) algorithm was applied to select white matter (regions of interest) within the brain and segment them from the surrounding brain structures. These regions of interest were processed by CNN models (ResNet101, DenseNet201, and MobileNet) to extract deep feature maps, which were then combined into fused feature vectors of multi-CNN model combinations (ResNet101-DenseNet201, DenseNet201-MobileNet, ResNet101-MobileNet, and ResNet101-DenseNet201-MobileNet). The multi-CNN features underwent dimensionality reduction using ACO and MESbS algorithms to remove unimportant features and retain important features. The XGBoost classifier employed the resultant feature vectors for classification. All developed hybrid systems displayed promising outcomes. For multi-class classification, the XGBoost model using ResNet101-DenseNet201-MobileNet features selected by ACO attained 99.4% accuracy, 99.45% precision, and 99.75% specificity, surpassing prior studies (93.76% accuracy). It reached 99.6% accuracy, 99.65% precision, and 99.55% specificity in binary-class classification. These results demonstrate the effectiveness of multi-CNN fusion with feature selection in improving MS classification accuracy.