Multimodal Pain Classification (m-PainAttnNet)
Extended PainAttnNet with attention-based fusion of ECG, EMG, and EDA signals, achieving 98.8% accuracy on 5-class pain intensity classification from the BioVid dataset.
Semantic Segmentation of Aerial Drone Images
Benchmarked UNet, DeepLabV3+, SegNet, and Swin Transformer on 4000×6000 px drone imagery across 23 semantic classes; UNet achieved best accuracy at 86.4%.