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Papers: 10.3390/brainsci11060809

https://doi.org/10.3390/brainsci11060809

A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features

Cited by: 3
Author(s): Ming-Chou Ho, Hsin-An Shen, Yi-Peng Eve Chang, Jun‐Cheng Weng
Published: about 4 years ago

Software Mentions 5

cran: kit
Data Manipulation Functions Implemented in C
Papers that mentioned: 42
Very Likely Science (75)
cran: mapping
Automatic Download, Linking, Manipulating Coordinates for Maps
Papers that mentioned: 37
Very Likely Science (77)
cran: REST
RcmdrPlugin Easy Script Templates
Papers that mentioned: 1,757
Very Likely Science (75)
pypi: kit
Flask, Celery, SQLAlchemy integration framework.
Papers that mentioned: 42
Very Likely Science (65)
pypi: mapping
Mappings for generic and tradeable futures instruments
Papers that mentioned: 37
Very Likely Science (65)