Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit

J, Ashok Kumar; Abirami, S.; Trueman, Tina Esther; Wang, Zhaoxia

doi:10.1016/j.neucom.2021.02.023

Cited by 36 publications

(6 citation statements)

References 23 publications

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“…Subsequently, a hybrid model is proposed to include strong features of both structures. The LSTM structure, which eliminates the problem of keeping long-range data in RNN structures in memory, has been utilized in efficiency estimation (Jayaraman, Murugappan, Trueman, and Cambria 2021).…”

Section: Methodsmentioning

confidence: 99%

Hybrid Deep Learning Implementation for Crop Yield Prediction

ÇETİNER

2023

Afyon Kocatepe University Journal of Sciences and Engineering

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Agriculture producers should be supported technologically in order to continue production in a way that meets the worldwide food supply and demand. Automatic realization of crop yield estimation calculation is a desired need of farmers. Automatic yield estimation also facilitates the work of agricultural producers with different goals such as imports and exports. To achieve the stated objectives, deep learning models have been developed that estimated yield using parameters such as the amount of water per hectare, the average amount of sunlight received by the hectare, the amount of fertilization per hectare, the number of pesticides used per hectare, and the area of cultivation. With the hybrid model created by combining the strengths of the LSTM and CNN models developed within the scope of this article, the success rate of data prediction has increased with fine adjustments. Success rates of 89.71 R2, 0.0035 MSE, 0.0248 RMSE, 0.0461 MAE, and 10.10 MAPE have been achieved with the Proposed hybrid model. This model is competitive with similar studies with the stated values.

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Section: Methodsmentioning

confidence: 99%

Hybrid Deep Learning Implementation for Crop Yield Prediction

ÇETİNER

2023

Afyon Kocatepe University Journal of Sciences and Engineering

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“…The forward GRU calculates in the normal time sequence, while the backward GRU processes the input sequence in reverse. Through this bidirectional propagation, the model can utilize future and past information to reference the current time step, thereby better capturing contextual relationships in sequential data [42][43][44]. Figure 2 illustrates the structure of Bi-GRU.…”

Section: B Analysis Of the Bi-gru Modelmentioning

confidence: 99%

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

Li,

Zhao

et al. 2024

IEEE Access

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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.

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“…The use of convolutional layers and LSTM in conjunction with word embeddings has shown promising results [47]. Data imbalance in toxicity datasets has been a significant challenge, with the majority of comments being non-toxic [48]. To address this, data augmentation techniques have been employed, including the creation of new comments and the substitution of words with synonyms [49].…”

Section: Literature Reviewmentioning

confidence: 99%

Graph convolution networks for social media trolls detection use deep feature extraction

Asif,

Al-Razgan,

Ali

et al. 2024

J Cloud Comp

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This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.

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Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit

Cited by 36 publications

References 23 publications

Hybrid Deep Learning Implementation for Crop Yield Prediction

Hybrid Deep Learning Implementation for Crop Yield Prediction

The Analysis of Deep Learning Recurrent Neural Network in English Grading Under the Internet of Things

Graph convolution networks for social media trolls detection use deep feature extraction

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