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Paper details
Number 2 - June 2022
Volume 32 - 2022
Hybrid deep learning model-based prediction of images related to cyberbullying
Mahmoud Elmezain, Amer Malki, Ibrahim Gad, El-Sayed Atlam
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.
Keywords
cyberbullying, ResNet50, MobileNetV2, support vector machine