In this paper, we propose spam e-mail filtering methods having high accuracies and low
time complexities. The methods are based on the n-gram approach and a heuristics which is …

We propose anti-spam filtering methods for agglutinative languages in general and for
Turkish in particular. The methods are dynamic and are based on Artificial Neural Networks …

We propose dynamic anti-spam filtering methods for agglutinative languages in general and
for Turkish in particular, based on Artificial Neural Networks (ANN) and Bayesian filters. The …

Spam has become an increasingly important problem with a big economic impact in society.
Spam filtering poses a special problem in text categorization, in which the defining …

In this paper, we propose a mechanism for filtering incoming spam mails by generating
spam mail prototypes using genetic algorithm. Firstly, words from e-mails are extracted and …

This paper evaluates five supervised learning methods in the context of statistical spam
filtering. We study the impact of different feature pruning methods and feature set sizes on …

This paper presents a content-based approach to spam detection based on low-level
information. Instead of the traditional'bag of words' representation, we use a'bag of character …

The increasing number of unsolicited e-mail messages (spam) reveals the need for the
development of reliable anti-spam filters. The vast majority of content-based techniques rely …

In this paper, a Turkish anti-spam filtering is implemented to determine text-based Turkish
spam e-mails (junk e-mail or bulk e-mail). Since e-mails are extremely easy and cheap to …

SPAM e-mails have a direct cost in terms of time, server storage space, network bandwidth
consumptions and indirect costs to protect privacy and security breaches. Efforts have been …