scholarly journals Evaluation of Machine Learning Techniques for Email Spam Classification

2021 ◽  
Vol 11 (4) ◽  
pp. 35-42
Author(s):  
Mahmoud Jazzar ◽  
◽  
Rasheed F. Yousef ◽  
Derar Eleyan
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Email Spam

scholarly journals Analysis of Naıve Bayes Algorithm for Email Spam Filtering

2021 ◽  
Vol 7 (01) ◽  
pp. 5-9
Author(s):  
RajKishore Sahni
Keyword(s):  
Machine Learning ◽  
Service Providers ◽  
Machine Learning Techniques ◽  
Research Trend ◽  
Learning Approaches ◽  
Spam Filtering ◽  
Internet Service ◽  
Learning Techniques ◽  
Bayes Algorithm ◽  
Email Spam

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep learning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails

A Comparative Analysis and Discussion of Email Spam Classification Methods Using Machine Learning Techniques

2019 ◽  
pp. 185-206 ◽  
Author(s):  
Aakash Atul Alurkar ◽  
Sourabh Bharat Ranade ◽  
Shreeya Vijay Joshi ◽  
Siddhesh Sanjay Ranade ◽  
Gitanjali R. Shinde ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
Learning Techniques ◽  
Email Spam

A proposed data science approach for email spam classification using machine learning techniques

2017 ◽  
Author(s):  
Aakash Atul Alurkar ◽  
Sourabh Bharat Ranade ◽  
Shreeya Vijay Joshi ◽  
Siddhesh Sanjay Ranade ◽  
Piyush A. Sonewar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Science Approach ◽  
Email Spam

scholarly journals A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques

2021 ◽  
Author(s):  
Simarjeet Kaur ◽  
Meenakshi Bansal ◽  
Ashok Kumar Bathla
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Spam Detection ◽  
Learning Techniques ◽  
E Mail ◽  
Email Spam

Due to the rise in the use of messaging and mailing services, spam detection tasks are of much greater importance than before. In such a set of communications, efficient classification is a comparatively onerous job. For an addressee or any email that the user does not want to have in his inbox, spam can be defined as redundant or trash email. After pre-processing and feature extraction, various machine learning algorithms were applied to a Spam base dataset from the UCI Machine Learning repository in order to classify incoming emails into two categories: spam and non-spam. The outcomes of various algorithms have been compared. This paper used random forest, naive bayes, support vector machine (SVM), logistic regression, and the k nearest (KNN) machine learning algorithm to successfully classify email spam messages. The main goal of this study is to improve the prediction accuracy of spam email filters.

scholarly journals A Study of Machine Learning Algorithms on Email Spam Classification

10.29007/qshd ◽  
2020 ◽  
Author(s):  
N Sutta ◽  
Z Liu ◽  
X Zhang
Keyword(s):  
Machine Learning ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Classification Models ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Comprehensive Comparison ◽  
Email Spam

Despite the fact that different techniques have been developed to filter spam, due to the spammer’s rapid adoption of new spam detection techniques, we are still overwhelmed with spam emails. Currently, machine learning techniques are the most effective ways to classify and filter spam emails. In this paper, a comprehensive comparison and analysis of the performance of various classification models on the 2007 TREC Public Spam Corpus are exhibited in various cases of without or with N- Grams as well as using separate or combined datasets. It is shown that the inclusion of the N-Grams in the pre-processing phase provides high accuracy results for classification models in most of the cases, and the models using the split approach with combined datasets give better results than models using the separate dataset.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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