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
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https://www.kaggle.com/datasets/venky73/spam-mails-dataset/?select=spam_ham_dataset.csv
https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
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Copyright (c) 2025 Arepalli Gopi, Sudha L.R, Iwin Thanakumar Joseph S (Author)