Email Monitoring System Using Various Machine Learning Approaches
Keywords:
Machine learning, Deep learning, Ensemble learning, Naive Bayes, Support Vector Machine, Convolutional neural network, AdaBoost Classifier and Long short-term memory.Abstract
A large portion of email traffic is made up of spam, which has caused issues throughout the world. Spammers always employ new techniques, making managing or preventing spam messages difficult. In today’s world, both businesses and educational institutions heavily rely on email communication. This study aims to compare the predictive performance of Machine Learning (ML), Deep Learning (DL), and Ensemble Learning (EL) in the context of email monitoring systems. In our research, we build upon previous studies addressing the spam problem to enhance accuracy. We employ a variety of methods, including Naive Bayes (NB), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Adaptive Boosting and Random Forest. The paper findings reveal that LSTM achieved the highest level of accuracy, reaching 99.88%. Consequently, LSTM stands out as a potent machine-learning system with potential benefits for future studies in this field.
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