“Advanced Analysis of Learning-Based Spam Email Filtering Methods Based on Feature Distribution Differences of Dataset.” has been added to your cart. View cart
Ā Ā Ā Ā 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.
Ā Ā Ā Ā Ā 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.