Nature

Machine learning for quantitative LIBS analysis of aluminum alloys: a comparison of random forest, gradient boosting, and extremely randomized trees

Laser-induced breakdown spectroscopy (LIBS) is a versatile technique for characterizing materials and analyzing elements, but it is limited in its quantitative accuracy due to nonlinear effects. This ...
Nature

Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach

Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. A real-world dataset ...
EurekAlert!

Overview of ML model workflows in pharmaceutical prediction tasks (IMAGE)

(a) Examples of three major ML model types: tree‐based models (e.g., random forests, gradient boosted trees), kernel‐based models (e.g., support vector machines, Gaussian processes), and deep learning ...