تجاوز إلى المحتوى الرئيسي

Efficient E-Mail Spam Detection Strategy Using Genetic Decision Tree Processing with NLP Features

Author name : AHMED IBRAHIM TALOBA MOHAMED
Publication Date : 2022-03-24
Journal Name : Computational Intelligence and Neuroscience

Abstract

In the modern era, the proliferation of unsolicited emails, commonly known as spam, poses significant challenges to email communication systems. Traditional spam detection methods often struggle to balance detection accuracy and computational efficiency. This paper introduces an efficient e-mail spam detection strategy utilizing a genetic decision tree algorithm. By integrating genetic algorithms with decision tree classifiers, the proposed method optimizes the selection of features and decision rules, leading to improved classification performance. Experimental results demonstrate that this approach achieves higher detection rates and lower false-positive rates compared to conventional techniques, making it a promising solution for real-time spam detection in dynamic email environments.

Keywords

E-mail Spam Detection, Genetic Algorithm, Decision Tree, Machine Learning, Classification These elements provide a concise overview of the paper's focus and scope.

Publication Link

https://doi.org/10.1155/2022/7710005

Block_researches_list_suggestions

Suggestions to read

HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things
Ahlem . Harchy Ep Berguiga
Generalized first approximation Matsumoto metric
AMR SOLIMAN MAHMOUD HASSAN
Structure–Performance Relationship of Novel Azo-Salicylaldehyde Disperse Dyes: Dyeing Optimization and Theoretical Insights
EBTSAM KHALEFAH H ALENEZY
“Synthesis and Characterization of SnO₂/α-Fe₂O₃, In₂O₃/α-Fe₂O₃, and ZnO/α-Fe₂O₃ Thin Films: Photocatalytic and Antibacterial Applications”
Asma Arfaoui
تواصل معنا