Enhancing the Optimization of BI-LSTM Classifier with Ensemble Methods, Regularization, and Cross-Validation Techniques for Email Spam Detection
Year: 2024
Type: article
Abstract: Email spam, a persistent and escalating issue, continues to disrupt the digital communication landscape, causing inconvenience and time loss for users worldwide. With technological advancements, spammers continually adapt and refine their tactics to infiltrate email inboxes. Staying current with state-of-the-art anti-spam techniques is imperative to secure emails and eliminate unwanted messages. Our research work embarks on an exploration of supercharging email spam detection through the augmentation of a Bidirectional Long Short-Term Memory (BI-LSTM) classifier. Our approach integrates ensemble methods, regularization techniques, and cross-validation into the fabric of the BI-LSTM model, creating a formidable spam detection system. Our paper delves into the intricate technical aspects of these methodologies, elucidating their synergy in fortifying the classifier's performance
Source: SAP Multidisciplinary Open
DOI: 10.62486/agmu202544
Institutions: Annamalai University, Koneru Lakshmaiah Education Foundation
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404016511