A simple DNN regression for the chemical composition in essential oil
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910749573513216 |
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| author | Harada, Yuki Maeda, Shuichi Kiyama, Masato Nakamura, Shinichiro |
| author_facet | Harada, Yuki Maeda, Shuichi Kiyama, Masato Nakamura, Shinichiro |
| contents | Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little attention, with the exception of a few prior studies. In this study, we configured three simple DNN regressors to predict essential oil property based on chemical composition. Despite showing overfitting due to the small size of dataset, all models were trained effectively in this study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12936 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A simple DNN regression for the chemical composition in essential oil Harada, Yuki Maeda, Shuichi Kiyama, Masato Nakamura, Shinichiro Machine Learning Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little attention, with the exception of a few prior studies. In this study, we configured three simple DNN regressors to predict essential oil property based on chemical composition. Despite showing overfitting due to the small size of dataset, all models were trained effectively in this study. |
| title | A simple DNN regression for the chemical composition in essential oil |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.12936 |