This study aims to improve the accuracy of spam email detection by leveraging the advanced contextual capabilities of the DistilBERT model for text classification.
Aim:
This study aims to develop an efficient and scalable system for multi-class classification of URLs into Phishing, Benign, Defacement, and Malware categories using the lightweight and context-aware DistilBERT model.
To improve the accuracy and efficiency of cyberbullying detection in social media text by utilizing an advanced machine learning model (DistilBERT) that overcomes ambiguity and classification challenges.
To improve the accuracy and efficiency of cyberbullying detection in social media text by utilizing an advanced machine learning model (DistilBERT) that overcomes ambiguity and classification challenges.