Focal Interest
Focal Interest

Random forest

  1. Pre-course aptitude tests predicted introductory programming performance
    Study develops pre-course aptitude test to predict introductory programming performance and identify students needing additional support using machine learning models.
  2. Geospatial foundation models improved tree species mapping accuracy
    Foundation models outperform conventional satellite methods for tree species classification in mountain forests, achieving high accuracy with minimal training data but requiring nonlinear classifiers.
  3. Support Vector Machine and Random Forest led software fault prediction
    Review of machine learning techniques for software fault prediction from 2023-2025, examining algorithms, datasets, evaluation methods, and challenges in predictive modeling for software reliability.
  4. Explainable models predicted dynamic responses of LPBF A286 lattices
    Explainable machine learning framework for predicting dynamic mechanical responses of LPBF-fabricated A286 lattice structures under high-strain-rate impact testing with interpretable design rules.