The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a corpus-based approach for extracting keywords from a text written in a language that has no word boundary. Based on the concept of Thai character cluster, a Thai running text is preliminarily segmented into a sequence of inseparable units, called TCCs. To enable the handling of a large-scaled
developed by implementing the keyword stripping using the Porter Stemmer algorithm. This could make the keyword search more efficient, as the root or stem word is only considered. Experimental results on two public spam corpuses are also discussed at the end.
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
part of a trending discussion topic by the presence of a tagged keyword. Relying solely on this keyword, however, may be inadequate for identifying all the discussion associated with a trend. Our research demonstrates that machine learning techniques can be used identify the top trend a tweet belongs to with up to 85
The explosive growth of unsolicited emails has prompted the development of numerous spam filtering techniques. A Bayesian spam filter is superior to a static keywordbased spam filter because it can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters can be
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.