Effect machine learning techniques for analyzing and filtering spam Mails problems
Tareq Abed Mohammed, Samara Khamees Jwair · 2021
The increase in spam and unwanted emails and messages has increased dramatically over the few past years to overcome this problem the need to develop more reliable and more powerful anti-spam filters. Modern machine learning techniques are applied to identify and filter spam messages. The research provide a symbolic review of different automated, learning-based spam filtering methods. This test provides details of the key concepts in spam filtering, testing, productivity, and search directions. The first discussion behind the study detect the application of machine learning approaches to the spam filtering methods for leading Internet service providers, such as spam filters in Gmail, Yahoo and Outlook. In this paper we provide the experiments about the normal spam filtering process and different work of researchers in finding spam using efficient ML techniques. Our research compares strengths and faults in current machine learning methods and clear research issues in spam filtering. We recommend in-depth and in-depth learning as future technologies that can accurately address the threat of spam. The necessity of effective spam filters increases. In this research, we also applied an efficient spam filter approaches to spam emails and messages based on Machine learning techniques such as Neural networks to remove the unwanted messages or raw data. The Neural Network Filtering feature evaluates the likelihood of different words appearing in Legitimate and spam mails and classifies them according to these possibilities.