A Machine Learning Based Email Spam Classification Framework Model: Related Challenges and Issues

Abstract

Spam emails, also known as non-self, are unsolicited commercial emails or fraudulent emails sent to a particular individual or company, or to a group of individuals. Machine learning algorithms in the area of spam filtering is commonly used. There has been a lot of effort to render spam filtering more efficient in classifying e-mails as either ham (valid messages) or spam (unwanted messages) through the ML classifiers. We may recognize the distinguishing features of the material of documents. Much important work has been carried out in the area of spam filtering which cannot be adapted to various conditions and problems which are limited to certain domains. Our analysis contrasts the positives methods as well as some shortcomings of current ML methods and open spam filters study challenges. We suggest some of the new ongoing approaches towards deep leaning as potential tactics that can tackle the challenge of spam emails efficiently.

Smart Spam Detection and Correction for Temperature in Heater

“…7.1. Linear Regression Linear regression is a type of supervised machine learning algorithm that computes the linear relationship between the dependent variable and one or more independent features by fitting a linear equation to observed data [5]. When there is only one independent variable, it is known as Simple Linear Regression and when there are multiple independent variables, it is known as Multiple Linear Regression.…”

A Novel Fuzzy-Logic-Based Multi-Criteria Metric for Performance Evaluation of Spam Email Detection Algorithms

“…Mallampati and Hegde [14] implement NB, J48 decision tree, and deep neural network. The performance of the three algorithms is evaluated using the Spam Assassin dataset, with accuracy, precision, and recall as the performance metrics.…”

“…In particular, artificial intelligence (AI)-based methods have received notable attention from researchers in recent years. Special emphasis has been placed on the methods based on machine learning [1,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In addition, deep learning methods have recently been successfully applied to spam email detection [10][11][12][13][14][26][27][28].…”

“…Special emphasis has been placed on the methods based on machine learning [1,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In addition, deep learning methods have recently been successfully applied to spam email detection [10][11][12][13][14][26][27][28]. The outcomes of these studies indicate that machine learning and deep learning algorithms provide an effective platform for efficiently solving the spam detection problem; yet there is still a room for further improvement and development.…”