Researchers have optimized a headspace sorptive extraction (HSSE) method coupled with gas chromatography-mass spectrometry ...

Abstract: The twin support vector machine (TWSVM) classifier and its fuzzy variant fuzzy twin support vector machine (FTSVM) have received considerable attention due to their low computational ...

ABSTRACT: From the perspective of student consumption behavior, a data-driven framework for screening student loan eligibility was developed using K-means clustering analysis and decision tree models.

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Alterations in brain structure have been suggested to be associated with bulimia nervosa (BN). This study aimed to employ machine learning (ML) methods based on diffusion tensor imaging (DTI) to ...

Ms. Mutcherson is a professor at Rutgers Law School. Right now in an Atlanta hospital room lies a 30-year-old nurse and mother, Adriana Smith. Ms. Smith, who is brain-dead, has been connected to life ...

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.

ABSTRACT: Making the distinction between different plantation tree species is crucial for creating reliable and trustworthy information, which is critical in forestry administration and upkeep. Over ...

Abstract: Electricity theft remains a significant challenge for PT PLN (Persero), Indonesia’s primary electricity provider, serving over 89 million customers as of 2023. The study focuses on ...