WEKO3
アイテム
Garbage Content Estimation Using Internet of Things and Machine Learning
http://hdl.handle.net/10061/0002000196
http://hdl.handle.net/10061/000200019614482693-f96b-47a4-bf0e-8b5765fec2a7
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-04-08 | |||||||
| タイトル | ||||||||
| タイトル | Garbage Content Estimation Using Internet of Things and Machine Learning | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | IoT-based smart garbage system | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | garbage content estimation | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | machine learning algorithms | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | journal article | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| 著者 |
Likotiko, Eunice
× Likotiko, Eunice
× 松田, 裕貴× 安本, 慶一 |
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Much garbage is produced daily in homes due to living activities, including cooking and eating. The garbage must be adequately managed for human well-being and environmental protection. Although the existing IoT-based smart garbage systems have gained high garbage classification accuracy, they still have a problem that they provide a small number of garbage categories, not enough for reasonable practices of household garbage separation. This study presents a new smart garbage bin system, SGBS, embedded with multiple sensors to solve the problem. We deployed temperature, humidity, and gas sensors to know the condition and identify the garbage content disposed of. Then, we introduce a new garbage content estimation method by training a machine learning model using daily collected fuse sensor readings combined with detailed household garbage contents annotations to perform garbage classification tasks. For evaluation, we deployed the designed SGBS in five households over one month. As a result, we confirmed that the leave-one-house cross-validation results showed an accuracy of 91% in 5 kitchen waste contents, also, 89% in 5 paper/softbox contents, and 85% in the 8 garbage categories for the classification tasks. | |||||||
| 書誌情報 |
en : IEEE Access 巻 11, p. 13000-13012, 発行日 2023-02-03 |
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| 出版者 | ||||||||
| 出版者 | IEEE | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2169-3536 | |||||||
| 出版者版DOI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1109/ACCESS.2023.3242547 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://ieeexplore.ieee.org/document/10036411 | |||||||
| 権利 | ||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||
| 権利情報 | IEEE is not the copyright holder of this material. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | NA | |||||||
