Abstract: This paper focuses on identifying the Arabic Lan-guage learners. The main contribution of the proposed method is to use a deep learning model based on the Gated Recurrent Unit Network (GRUN). The proposed model explores a multitude of stylistic features such as the syntax, the lexical and the n-grams ones. To the best of our awareness, the obtained results outperform those obtained by the best existing systems. Our accuracy is the best comparing with the pioneers (45% vs 41%), considering the limited data and the unavailability of accurate tools dedicated to the Arabic language.

Seifeddine Mechti, Roobaea Alroobaea, Moez Krichen, Saeed Rubaiee and Anas Ahmed, “Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110576

@article{Mechti2020,
title = {Deep Learning Model for Identifying the Arabic Language Learners based on Gated Recurrent Unit Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110576},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110576},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Seifeddine Mechti and Roobaea Alroobaea and Moez Krichen and Saeed Rubaiee and Anas Ahmed}
}