An Internet of Things based Waste Management System using Hybrid Machine Learning Technique
M. S. Arunkumar, P Sathishkumar, R Suguna, Simon Deepa
Abstract
The most significant aspects of creating smart cities is waste management. Recycling and landfilling are two methods of waste management that lead to the demolition of trash. Because of population expansion, it is difficult to maintain cleanliness in urban areas. Because the machine learning (ML) and Internet of Things (IoT) eases the gathering, integration, and processing of diverse kinds of information, it provides an agile solution for classification and real-time monitoring. It is our intention to create a waste management scheme based on the IoT. The IoT has been used to keep tabs on people's movements and to help with garbage management. A machine learning technique called Decision Tree with Extreme Learning Machine was used to analyze data about a city (DT-ELM). The single classifier requires iterative training, which is time consuming, but the suggested hybrid model does not. Decision trees use traits that are good at classifying. Additional weights for the selected features are calculated to improve their categorization accuracy. We use the entropy theory to map the decision tree to ELM in order to get accurate prediction results. The garbage kind, truck size, and waste source may all be analyzed thanks to the network. In order to take the proper action, the waste management centers were informed of this information. An experiment was conducted to test the efficiency of an IoT -based trash management system.