Classification Analysis for e-mail Spam using Machine Learning and Feed Forward Neural Network Approaches
Srinivasa Rao Dangeti, Dileep Kumar Kadali, Yesujyothi Yerramsetti, Ch Raja Rajeswari, D. Venkata Naga Raju, Srinath Ravuri · Advances in computer science research · 2024
In the present era, electronic communication plays an essential role in our daily lives.However, this convenience is accompanied by the persistent challenge of email spam, which inundates inboxes and poses a serious cybersecurity threat.Email spam remains a pervasive issue, with conventional spam filters often struggling to adapt to evolving spamming techniques.This paper aims to leverage machine learning advanced techniques to enhance the accuracy and efficiency of email spam classification.By employing state-of-the-art algorithms and models, the goal is to develop a robust and adaptable system capable of effectively identifying and filtering out spam emails.Several machine learning classifiers namely KNN, SVC, DT, NB, RF and Logistic Regression are applied.Later, a deep learning Feed Forward Neural Network model was applied and achieved good accuracy.The experiments' outcome showed that the proposed deep learning gave good accuracy for email spam classification.