| Title | Machine learning approach for a circular economy with waste recycling in smart cities |
|---|---|
| ID_Doc | 20520 |
| Authors | Chen, XR |
| Title | Machine learning approach for a circular economy with waste recycling in smart cities |
| Year | 2022 |
| Published | |
| Abstract | The information and communication technology (ICT) makes the smart city exchange information with the general public and deliver higher-quality services to citizens. The collection of waste is important in smart city service and smart technology has great potential for increasing garbage collection efficiency and quality all around the world. This is a waste of resources since garbage is emptied even half-full, city resources are misused, and vehicle gas is wasted. High costs and poor effectiveness are the two main problems with smart city garbage collection. This problem can be solved by recycling since it reduces the garbage that has to be disposed of and protects valuable storage space. Although the recycling rates are rising, estimates predict that people can generate more waste than ever before. A key focus of machine learning is developing algorithms that can acquire and utilize information in the learning process to make future predictions. Therefore, this paper's automatic machine learning-based waste recycling framework (AMLWRF) has been proposed to classify and separate materials in a mixed recycling application for improved separation of complicated waste. The major goal of this research is to examine machine learning algorithms utilized in recycling systems. This paper suggests the ML and Internet of Things (IoT) for smart waste management to overcome this problem in the smart city. The IoT-powered devices may be installed in waste containers including recycling bins and it gives real-time data on garbage generating behavior. Image processing may be used to calculate a dump's garbage index. They offer a clear picture of trash and recycling trends and suggestions on improving productivity. This research compiles the most recent advances in recycling-related machine learning.(c) 2022 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| https://doi.org/10.1016/j.egyr.2022.01.193 |
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