ENHANCING SUSTAINABLE DEVELOPMENT THROUGH COMMUNITY ENGAGEMENT

Description

As AI continues to expand and evolve, it will play an increasingly important role in shaping the future of communications. In this thesis after commenting on AI and its benefits in communications, we will focus on solving the email classification problem with a proprietary method using ten machine learning algorithms, and comment on the produced results.
This thesis focuses on the security of electronic mail, using machine learning algorithms. Spam email is unwanted messages, usually commercial, sent to many recipients.
In this work, an algorithm for the detection of spam messages with the aid of machine learning methods is proposed. The algorithm accepts as input text email messages grouped as benevolent (“ham”) and malevolent (spam) and produces a text file in csv format. This file then is used to train a bunch of ten Machine Learning techniques to classify incoming emails into ham or spam. The following Machine Learning techniques have been tested: Support Vector Machines, k-Nearest Neighbor, Naïve Bayes, Neural Networks, Recurrent Neural Networks, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression and Decision Trees.