The researchers report that their model achieved a 93% cross-validation score in binary classification, underscoring its ...

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The ...

Researchers developed a two-stage ML model to predict coating degradation by linking environmental factors to physical changes and corrosion. The framework enables more accurate, data-driven ...

Multiclass classification is one of the most fundamental tasks in data mining. However, traditional data mining methods rely on the model assumption, they generally can suffer from the overfitting ...

LCGC International interviewed Bob Pirok from the University of Amsterdam, Netherlands to discuss strategies for enhancing ...

Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

Recent advances in deep learning have significantly transformed mineral classification methodologies, supplanting labour‐intensive manual approaches with automated, high-precision systems.

Students at a leading London private school are studying a drag queen’s memoir and works by non-binary authors as part of a push to “diversify” the English curriculum. Staff at Alleyn’s ...

This project focuses on detecting and classifying faults in a DC microgrid system using Support Vector Classification (SVC), a machine learning technique known for its robustness in binary and ...