Paper Contents
Abstract
Abstract- Topic: Dee Learnin Techniaues for Garba e Classification Officials in developing countries like India usually acknowledge the need for better management. However, little efforts are done to improve the situation, and changes take a long period of time. As we know, India's population is equivalent to 17.7% of the total population. With the rise of development of smart cities across India, a Smart Garbage Management system is very necessary. Since the amount of waste is multiplying day by day. It is essential to bring the best approach to manage this problem because the generated waste exceeds 2 billion tones. The existing gms in India practices collection of domestic and industrial waste and dumping into big dumping yards. Solid waste separation is done by laborers which is not so systematic, consumes a lot of time and it is not even completely feasible due to large amounts of garbage. The purpose of this research is to build a real time application which recognizes the type of waste and categorize it into defined categories. By implementing this Trashnet classification system ,we want to reduce the physical efforts and effectively segregate the waste into different categories. The model used for this study is Convolution Neural Network (CNN), a Machine Learning algorithm which is used on a dataset containing images of garbage. This system ensures a best way for waste management and will also speed up the segregation process with higher accuracy. This study lasts with remarkable results and is successful to classify various images of waste in correct classes.
Copyright
Copyright © 2025 AYYOLLU SHRAVAN KUMAR. This is an open access article distributed under the Creative Commons Attribution License.
