Artificial Intelligence-Based Email Spam Filtering
Year: 2024
Type: article
Abstract: Email spamming has become a big issue in spreading unsolicited emails. This project focuses on tackling the prevalent issue of email spam through the implementation of machine learning techniques, particularly emphasizing spam filtering using artificial intelligence (AI). The goal is to develop an AI-powered application for efficient identification and filtration of spam emails. Key functionalities include email content preprocessing, feature extraction using techniques like Count Vectorization and TF-IDF Vectorization, and deploying machine learning models such as Support Vector Machines, Naive Bayes, and K-Nearest Neighbour classifiers. The results demonstrate an impressive 98.65% accuracy in recognizing spam emails. The conclusion highlights the significance of AI in spam filtering for accurate and reliable outcomes, addressing the persistent problem of email spam. This research contributes to advancing spam filtering techniques, serving as a valuable reference for future email security research and development.
Authors: Low Choon Keat, Tan Xuan Ying
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893158039