1. Classification of Sythetic Aperture Radar Images of Icebergs and Ships Using Random Forests Outperforms Convolutions Neural Networks
Conference Paper: IEEE Radar Conference 2020
Github: https://github.com/billyl320/iceberg_ships
2. Pill Shape Classification using Imbalanced Data with Human-Machine Hybrid Explainable Model
US Patent Application – George Mason University Link: https://ott.gmu.edu/wp-content/uploads/2020/05/GMU-20-010_Model-to-Classify-Pill-Shapes_Fact-Sheet.pdf
Github: https://github.com/billyl320/human_decision_tree_pills
3. SVM Model for Blood Cell Classification using Interpretable Features Outperforms CNN Based Approaches
Journal Article: Computer Methods and Programs in Biomedicine Update
Github: https://github.com/billyl320/bccd_svm