<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students’ academic performance. The results help stakeholders to take necessary measures against bullies and increase the community’s awareness of this phenomenon.</p>
</abstract>

Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students’ academic performance. The results help stakeholders to take necessary measures against bullies and increase the community’s awareness of this phenomenon.

Hybrid Deep Learning Model–Based Prediction of Images Related to Cyberbullying

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the...

Hybrid Deep Learning Model–Based Prediction of Images Related to Cyberbullying

Publié en ligne: 04 juil. 2022

Pages: 323 - 334

Reçu: 06 déc. 2021

Accepté: 07 mars 2022

DOI: https://doi.org/10.34768/amcs-2022-0024

Mots clés
cyberbullying, ResNet50, MobileNetV2, support vector machine

© 2022 Mahmoud Elmezain et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hybrid Deep Learning Model–Based Prediction of Images Related to Cyberbullying

Publié en ligne: 04 juil. 2022

Pages: 323 - 334

Reçu: 06 déc. 2021

Accepté: 07 mars 2022

DOI: https://doi.org/10.34768/amcs-2022-0024

Mots cléscyberbullying, ResNet50, MobileNetV2, support vector machine

© 2022 Mahmoud Elmezain et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
cyberbullying, ResNet50, MobileNetV2, support vector machine