报告详情
Optimized GhostNet and Modified Transformer for Cyberbullying Multi-Class Classification
编号：14
访问权限：仅限参会人
更新：2026-07-22 16:09:03 浏览：23次
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摘要
Cyberbullying has become serious social issue with the rapid growth of social media platforms. Many current approaches rely on heavyweight pretrained networks and complex hybrid architectures, resulting in increased memory usage, long training times, and limited suitability for real-time applications. To overcome these challenges, this paper presents a lightweight and efficient hybrid DL framework for multi-class image-based cyberbullying classification, named GGTNET. Initially, an improved median bilateral filter is applied to the input images to effectively remove noise. Then, data augmentation techniques like horizontal flipping as well as random rotation can be utilized. For feature extraction, Residual GhostNet integrated with enhanced coordinate attention (RGNC) is utilized as a lightweight backbone to capture discriminative spatial features. Parallel analysis of the collected features is performed using a Gated Recurrent Unit (GRU) and a modified Transformer architecture. A softmax classifier receives the concatenated outputs of both models for multi-class cyberbullying categorization. Additionally, the proposed network's hyperparameters are adjusted using enhanced hippopotamus optimization algorithm. The experimental analysis demonstrates achieves superior performance such as an accuracy of 99.10%.
关键词
Cyberbullying,GhostNet,Gated Recurrent Unit,Transformer and Deep learning
报告人
Soma Srivastava
PhD Research Scholar National Institute of Technology Patna稿件作者
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重要日期
-
会议日期
07月30日
2026
至08月01日
2026
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07月28日 2026
注册截止日期
-
07月30日 2026
初稿截稿日期
主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICNCCom 2026
- ic******@usssociety.org
历届会议
-
2026年07月30日 印度 Trichy
2026 International Conference on Networks Computers and Communications
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