| dc.description.abstract | Waste classification and processing are important issues in promoting a more
sustainable economy and friendly living environment. Smart technology
applications could change how waste could be handled and recycled to increase
productivity and reduce the risks of this manual work. Several studies have
applied different methods of deep learning for waste classification have been
proposed. Most studies have to combine many methods to improve the deep
learning model to become the most optimal. In this research, I focus on three
groups of typical recyclables in Vietnam and build a model which was applied
to a popular convolutional neural network CNN (Resnet50) by using the smartest
approaches. By using pre-trained models by transfer learning method, and
applying SGD optimizer instead of Adam optimizer, the results are obtained with
relatively high accuracy of 87.05%. This could show that our model will partially
help convert manual processing to automated system applications. | en_US |
