Paper Contents
Abstract
These days, machine learning algorithms are effectively used to automatically screen spam emails. In this paper, we analyze several popular machine learning methods and explain their efficacy in detecting spam emails. Email is one of the most popular forms of communication since it is easily accessible, enables quick message exchanges, and has a low transmission cost. Email is the fastest and least expensive way to communicate. Spam in emails is one of the most challenging problems with email systems. Spam emails are unsolicited, unsolicited messages delivered for commercial, fraudulent, or other purposes that are not addressed to a specific recipient. Many methods of detection and filtering are used to keep the spam under control. The KNN algorithm, which is a content-based approach, is one of the most practical and straightforward methods. For the purpose of folder and subject classifications in this study, a sizable collection of personal emails was first employed. To make the next KNN algorithm more time-efficient, it is improved and expanded to I-KNN. Later, this improved algorithm is applied as a whole to produce superior Email Spam Detection results. Java is used for the implementation, and the parameters are calculation time and similarity score. Obtained result shows that the efficiency of proposed approach is better than traditional approach.
Copyright
Copyright © 2023 Manish Kumar Sen, Pankaj Richhariya. This is an open access article distributed under the Creative Commons Attribution License.
