In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …

Machine learning is rapidly changing the cybersecu-rity landscape. The use of predictive
models to detect malicious activity and identify inscrutable attack patterns is providing levels …

Curcumin (Cur) is a traditional herb with known anticancer properties against various
malignancies such as breast cancer. In this study, a metal-organic framework (MOF) based …

An efficient approach for improving the predictive understanding of dynamic mechanical
system variability is developed in this work. The approach requires low model assessment …

This study introduces a universal correlation based on the modified version of the Arrhenius
equation to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide (CO2) …

Research into machine learning methods for fraud detection is of paramount importance,
largely due to the substantial financial implications associated with fraudulent activities. Our …

This study aimed to model the minimum spouting velocity (U ms) of vegetable biomasses in
conical spouted beds including five biomasses. A statistical analysis of the literature …

Training a machine learning algorithm from a class-imbalanced dataset is an inherently
challenging task. The task becomes more challenging when compounded by high …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Classification datasets created from chemical processes can be affected by errors, which
impair the accuracy of the models built. This fact highlights the importance of analyzing the …