Content-Based Spam Classification of Academic E-mails: A Machine Learning Approach

Content-Based Spam Classification of Academic E-mails: A Machine Learning Approach

Wahab Abdul Iddrisu, Sylvester Kwasi Adjei-Gyabaa, Isaac Akoto

In the academic environment, players have overstretched University faculty with less available time. The task of reading and deleting electronic mail (e-mail) spam tends to consume or steal the little available time they have at their disposal. Due to the spam issue, automated processes or methods for separating spam from valid emails are becoming important. Due to the unstructured nature of the material, additional features, and a vast number of documents, the process of automatically classifying spam email presents significant difficulties. Increasing usage of the e-mail spam directly affects the performance of these spam classifications with regards to the quality and speed based on the challenges stated above. Most of the recent algorithms consider only relevant features or characteristics for the classification of the e-mails as spam or legitimate. The main objective of this work was to use a machine-learning …

Springer Nature Singapore

2023

https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ECTxVnYAAAAJ&cstart=20&pagesize=80&citation_for_view=ECTxVnYAAAAJ:ufrVoPGSRksC

English