Abstract: A precise change detection in the multi-temporal optical images is considered as a crucial task. Although a variety of machine learning-based change detection algorithms have been proposed ...

Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...

Abstract: This study evaluates the performance of using machine learning models; J48 and Random Forest to classify bananas quality. The existing methods of visual inspection are qualitative and take ...

ABSTRACT: This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, ...

ABSTRACT: The advent of the internet, as we all know, has brought about a significant change in human interaction and business operations around the world; yet, this evolution has also been marked by ...

Background: The “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning ...

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class generally has a higher misclassification rate.

@IvanNardi As per our initial discussion: Is your feature request related to a problem? Please describe. Detecting malware and covert communications within encrypted traffic, especially when ...