A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...
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: Random forest regression is a widely used machine learning algorithm. In this study, random forest regression is employed to predict groundwater levels. Five influencing factors are ...
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 ...
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 ...
ABSTRACT: Road traffic accidents are one of the global safety and socioeconomic challenges. According to WHO (2024), it has caused over 1.19 million annual fatalities. It is also projected to cause ...
The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...
ABSTRACT: The stock market faces persistent challenges, including inefficiencies, volatility, and barriers to entry, which hinder its accessibility and reliability for investors. This paper explores ...
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