Convolutional Autoencoder for Email Spam Detection
Disciplines
Artificial Intelligence and Robotics
Abstract (300 words maximum)
In this paper, I talk about a novel technique for email spam detection. Using the extremely adept pattern recognition abilities of Autoencoders, I designed a Convolutional Autoencoder network to analyze and classify emails as either ham (legitimate) or spam (illegitimate/scam) emails. With promising results, this type of model could help revolutionize email spam detection and tagging, making everyone’s inbox less crowded with emails they don’t want to read.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Md Abdullah Al Hafiz Khan
Convolutional Autoencoder for Email Spam Detection
In this paper, I talk about a novel technique for email spam detection. Using the extremely adept pattern recognition abilities of Autoencoders, I designed a Convolutional Autoencoder network to analyze and classify emails as either ham (legitimate) or spam (illegitimate/scam) emails. With promising results, this type of model could help revolutionize email spam detection and tagging, making everyone’s inbox less crowded with emails they don’t want to read.