Enhancing Email Spam Detection through Artificial Intelligence

This technical report explores advanced artificial intelligence approaches to enhance email spam detection systems. The research investigates various machine learning and deep learning models to improve accuracy and reduce false positives in spam classification.

Research Methodology

The study employed a comprehensive approach including:

• Analysis of existing spam detection algorithms
• Development of novel feature extraction techniques
• Implementation of ensemble learning methods
• Evaluation using standard benchmark datasets

Key Findings

The research demonstrated significant improvements in spam detection accuracy through the application of transformer-based models combined with traditional feature engineering approaches.