Abstract
The process of drying rubber requires constant monitoring of rubber humidity. The dry rubber at the end of the drying process must have very low humidity and not be overheated. Modern drying process uses heat chamber to remove moisture from rubber. Opening the heat chamber to check the humidity of rubber often is not considered wise in terms of energy saving. However, we can install a camera inside the heat chamber to take the photo of the rubber in order to use the color of the rubber to determine the humidity of the rubber. In this research, we successfully use machine learning classification techniques to classify the color of rubber to humidity level. We found that K-nearest neighbor performs best given the data from our experiments.
| Original language | English |
|---|---|
| Title of host publication | 2022 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781665485104 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022 - Bangkok, Thailand Duration: 22 Jun 2022 → 25 Jun 2022 |
Publication series
| Name | 2022 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022 |
|---|
Conference
| Conference | 19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022 |
|---|---|
| Country/Territory | Thailand |
| City | Bangkok |
| Period | 22/06/22 → 25/06/22 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- classification
- color differentiation
- humidity
- machine learning
- rubber
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