| Title |
Waste Management of Residential Society using Machine Learning and IoT Approach |
| ID_Doc |
44643 |
| Authors |
Dubey, S; Singh, MK; Singh, P; Aggarwal, S |
| Title |
Waste Management of Residential Society using Machine Learning and IoT Approach |
| Year |
2020 |
| Published |
|
| DOI |
10.1109/esci48226.2020.9167526 |
| Abstract |
Due to the increasing population and industrialization of nations, waste management has become a challenging issue for all of us. A small scale waste management is also adding same potential as large scale waste management. IoT and machine learning based waste management system for residential society are aimed to enhance the same concern as the waste management of smart city. This paper employs on monitoring of various dustbins located at different residential societies. Dustbin is equipped with sensors which monitors for dustbin capacity, metal level and poisonous gas level. The machine learning classification technique such as SVM, NB, RF, DT and KNN are used to test their ability to predict the accuracy of sending alert messages to third party in order to manage the waste of the society. In addition, results suggest that RF algorithm produced the most accurate forecasts of the alert message. The accuracy of RF algorithm is 85.29 %. The overall impact of this research is in the upliftment of the green technologies by reducing pollution of the smart city. |
| Author Keywords |
Machine Learning; Waste Management; Random Forest; Decision Tree; IoT |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000670589300057 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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