A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features
- DOI
- 10.3390/brainsci11060809
- Published
- 2021-06-18
- Container
- Brain Sciences
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/brainsci11060809,
title = {A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features},
author = {Ming-Chou Ho and Hsin-An Shen and Yi-Peng Eve Chang and Jun-Cheng Weng},
year = {2021},
journal = {Brain Sciences},
doi = {10.3390/brainsci11060809},
url = {https://doi.org/10.3390/brainsci11060809}
}RIS
TY - JOUR TI - A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features AU - Ming-Chou Ho AU - Hsin-An Shen AU - Yi-Peng Eve Chang AU - Jun-Cheng Weng PY - 2021 JO - Brain Sciences DO - 10.3390/brainsci11060809 UR - https://doi.org/10.3390/brainsci11060809 ER -
APA
Ho, M., Shen, H., Chang, Y. E., & Weng, J. (2021). A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features. Brain Sciences. https://doi.org/10.3390/brainsci11060809
Source records
- crossref · retrieved 2026-09-25T11:12:04.438Z