Traditional machine learning methods like Support Vector Machines, Random Forest, and gradient boosting have shown strong performance in classifying device behaviors and detecting botnet activity.

Bankruptcy prediction has traditionally relied on statistical approaches such as Altman’s Z-score, which use financial ratios ...

Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management.

The study, titled "Machine Learning Technique for Carbon Sequestration Estimation of Mango Orchards Area Using Sentinel-2 Data," is led by Prof. Sittichai Choosumrong from the Department of Natural ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the random forest regression technique (and a variant called bagging regression), where the goal is to ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...