College of Engineering
Objectives or Summary:
In this study, we demonstrate that utilizing several pretrained convolutional neural
network models, such as ResNet18, ResNet50, ResNet101, Mobilenetv2 and
Shufflenet is feasible to anticipate fine particulate matter (PM2.5) concentrations
with minimal computation time. The results show that, it is possible to estimate the
PM2.5 level through pretrained models using a small single scene dataset.
This is a seminar about our published paper on 2023-06-21