IEEE

Improving the Detection Accuracy of Neurological Disorder through EEG-Based Analysis Leveraging Random Forest Classifier for Enhanced Accuracy Compared to K-Nearest Neighbours ...

Abstract: The aim of the proposed research is to enhance the accuracy of the detection of neurological disorders through the use of advanced machine learning on the electroencephalogram (EEG) data.
Frontiers

Advancing standardized cattle behavior classification with a random forest model evaluated across diverse datasets

1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, United States 2 Department of Animal Sciences, Cornell University, Ithaca, NY, United States The lack of ...
IEEE

Detecting HLS Hardware Trojans using Random Forest Classifier

Abstract: High-level Synthesis (HLS) generated IP designs are widely and effectively used in several image/video processing applications, consumer electronics applications as well as multimedia ...