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Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Nai??ve Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics to
With a growing number of Web documents, many approaches have been proposed for knowledge discovery on Web documents. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web
distinctive spam keywords. We investigate two ways of detecting such spams: 1) By comparing the similarity between the publisher posts and user comments, and 2) by learning a single representative meta-feature such as user name or ID. The first measure relieves us from repetitively learning a set of domain-dependent spam
and miss important emails from important people. This email management issue imposes an adverse effect on the productivity of email communication. Although many email clients today are equipped with tools to filter emails based on keywords, email addresses; most of these filters are static and are not updated
EMMA is an e-mail management assistant based on ripple down rules, providing a high degree of classification accuracy while simplifying the task of maintaining the consistency of the rule base. A naive Bayes algorithm is used to improve the usability of EMMA by suggesting keywords to help the user define rules. In
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.