Abstract:
"Effective waste management and recycling plays a pivotal role in mitigating environmental
pollution and fostering sustainability. Existing waste classification methods employing
machine learning can identify waste categories such as glass, cardboard, and metal; however,
they often fall short in differentiating recyclable from non-recyclable items within these
categories. This study aims to investigate the development of an advanced waste classification
model capable of not only identifying waste categories but also determining their recyclability
status using deep Convolutional Neural Networks (CNNs)"
