The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things
This work aims to investigate the use of the Recurrent Neural Network (RNN) in automated English grading. In order to achieve this, this work first constructs an automated English grading system based on the Internet of Things (IoT). Next, based on the variant of RNN called Gated Recurrent Unit (GRU...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/112902/ http://psasir.upm.edu.my/id/eprint/112902/1/112902.pdf |
| _version_ | 1848866071268818944 |
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| author | Li, Dandan Li, Wenling Zhao, Yanmei Liu, Xutao |
| author_facet | Li, Dandan Li, Wenling Zhao, Yanmei Liu, Xutao |
| author_sort | Li, Dandan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This work aims to investigate the use of the Recurrent Neural Network (RNN) in automated English grading. In order to achieve this, this work first constructs an automated English grading system based on the Internet of Things (IoT). Next, based on the variant of RNN called Gated Recurrent Unit (GRU), it introduces a self-attention mechanism into bidirectional GRU to form the Bidirectional-GRU_self-attention (Bi-GRU_Att) model. Simultaneously, an attention pooling (AP) mechanism is introduced into bidirectional GRU to form the Bidirectional-GRU_AP (Bi-GRU_AP) model. Comparative experiments are conducted using Chinese and English corpora to compare the performance of these two models. The results indicate that the Bi-GRU_AP model performs well on both Chinese and English datasets. On the Chinese dataset, compared to Bi-GRU_Att, Bi-GRU, and GRU, its accuracy is improved by 1.3%, 9.9%, and 19%, respectively. On the English dataset, compared to Bi-GRU_Att, Bi-GRU, and GRU, its accuracy is improved by 2.2%, 9.8%, and 19.2%, respectively. This suggests that introducing the AP module enables the model to better capture sentence information, thereby enhancing model performance. Additionally, after 20 iterations, the Bi-GRU_AP model exhibits good convergence and stability. The findings provide new insights for the development of automated English subjective grading systems based on IoT and deep learning. © 2013 IEEE. |
| first_indexed | 2025-11-15T14:14:46Z |
| format | Article |
| id | upm-112902 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:14:46Z |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1129022024-10-28T07:48:51Z http://psasir.upm.edu.my/id/eprint/112902/ The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things Li, Dandan Li, Wenling Zhao, Yanmei Liu, Xutao This work aims to investigate the use of the Recurrent Neural Network (RNN) in automated English grading. In order to achieve this, this work first constructs an automated English grading system based on the Internet of Things (IoT). Next, based on the variant of RNN called Gated Recurrent Unit (GRU), it introduces a self-attention mechanism into bidirectional GRU to form the Bidirectional-GRU_self-attention (Bi-GRU_Att) model. Simultaneously, an attention pooling (AP) mechanism is introduced into bidirectional GRU to form the Bidirectional-GRU_AP (Bi-GRU_AP) model. Comparative experiments are conducted using Chinese and English corpora to compare the performance of these two models. The results indicate that the Bi-GRU_AP model performs well on both Chinese and English datasets. On the Chinese dataset, compared to Bi-GRU_Att, Bi-GRU, and GRU, its accuracy is improved by 1.3%, 9.9%, and 19%, respectively. On the English dataset, compared to Bi-GRU_Att, Bi-GRU, and GRU, its accuracy is improved by 2.2%, 9.8%, and 19.2%, respectively. This suggests that introducing the AP module enables the model to better capture sentence information, thereby enhancing model performance. Additionally, after 20 iterations, the Bi-GRU_AP model exhibits good convergence and stability. The findings provide new insights for the development of automated English subjective grading systems based on IoT and deep learning. © 2013 IEEE. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/112902/1/112902.pdf Li, Dandan and Li, Wenling and Zhao, Yanmei and Liu, Xutao (2024) The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things. IEEE Access, 12. pp. 44640-44647. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10477404 10.1109/ACCESS.2024.3380480 |
| spellingShingle | Li, Dandan Li, Wenling Zhao, Yanmei Liu, Xutao The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things |
| title | The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things |
| title_full | The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things |
| title_fullStr | The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things |
| title_full_unstemmed | The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things |
| title_short | The analysis ofdeep learning Recurrent Neural Network in English grading under the Internet of Things |
| title_sort | analysis ofdeep learning recurrent neural network in english grading under the internet of things |
| url | http://psasir.upm.edu.my/id/eprint/112902/ http://psasir.upm.edu.my/id/eprint/112902/ http://psasir.upm.edu.my/id/eprint/112902/ http://psasir.upm.edu.my/id/eprint/112902/1/112902.pdf |