Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document ...

Researchers at The University of Texas at Arlington have developed a new computational tool that helps scientists pinpoint ...

To address this challenge, researchers developed SAVANA, a new algorithm, which they recently described in the journal Nature Methods. SAVANA uses machine learning to accurately identify ...

algorithms are widely adopted to address flexible job-shop scheduling problems (FJSPs) because of the optimization ability. However, traditional learning DEs are not sufficient to develop the feature ...

Abstract: Logistic regression is a well known classification method in the field of statistical learning. Recently, a kernelized version of logistic regression has become very popular, because it ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada ...

Methods: In this study, GC–MS-based metabolomics of Yangxian colored rice varieties were performed to characterize their volatile metabolites through multivariate statistics and machine learning ...