Hybrid Deep Learning – Based Email Spam Detection and De-Duplication

Abstract

Rapid growth of digital data has created serious storage inefficiency due to duplicate files and increasing email overflow, particularly under Gmail storage limits, which disrupt communication. Deep learning enables intelligent automation for large-scale data management through accurate pattern recognition in emails and files, improving spam detection and duplicate identification. However, existing solutions depend on separate tools for local duplicate file removal and manual email cleanup, with no unified platform connecting local file management and Gmail monitoring, increasing the risk of accidental data loss. To address these challenges, this paper proposes D-SPARC — Spam Detection and Redundancy Control Framework, a deep learning–based integrated system that performs efficient spam filtering and duplicate data detection within a single platform. The system continuously monitors Gmail storage, automatically removes redundant data, filters spam emails, and optimizes storage utilization to ensure uninterrupted and efficient communication.