Virual Oral Presentation Slide by Slide

IEEE/ICACT20250142 Slide.18        [Big Slide]	Oral Presentation
	Conclusion In this study, we proposed an AI-driven smishing detection model for Android devices, leveraging TinyBERT for feature extraction and Aquila Optimization (AO) for hyperparameter tuning. The model was trained on a publicly available SMS dataset from Kaggle, with rigorous preprocessing to improve message clarity. Visualization of TinyBERT embeddings using t-SNE demonstrated clear feature separability between legitimate and spam messages. The optimized model achieved superior performance, with 96.81% accuracy, 96.76% precision, 96.81% recall, and a 96.71% F1 score, outperforming standard models like GRU, LSTM, and SVM.

[Go to Next Slide]

Select Voice: Virtual Oral Presentation!!