Sharif Digital Repository

Studies show that a large proportion of sent emails are spam. Spam is one of the major problems of e-mail users that result in wasting time and cost. To overcome this problem different ways are used, one of the best ways is detecting spam based on their contents. Separating legitimate e-mails and spam within their contents can be categorized as text classification. So machine-learning approaches are extremely applied in text classification, that machine-learning algorithms can be used for spam classification. However, in the majority of these algorithms, training phase is in a batch. Whereas using incremental learning algorithms is preferred in many applications, especially spam detections.... 

Spam mails are unwanted mails sent to large numbers of users. Such emails not only consume the network resources but also cause lots of security uncertainties. Based on our observation, the location where the spam filters operate in is an important parameter to save network resources. In this thesis, we introduce a new and effective approach to avoid spam emails from being transferred by relocating the filtering system in the sender mail server. Compared with previous methods in our method, the spam email is detected after spammer clicks on the send button. Here the sender mail server decides based on some pre-defined criteria. If the sender mail server determines that the email is not spam,... 

As part of the definition of concept drift as an online learning task, concepts change or drift as time goes by. Consequently, these changes have to be monitored and their implication for learning should be recognized. An example of concept drift detection is needed for spam filtering problem. An effective spam filter must be able to handle various changes, including changes in the user’s criteria for filtering spam, changes in message topics, and changes caused by the people sending spam messages. In this thesis, spam detection system has been considered in which emails are given sequentially and learns them one by one. As we mentioned, the purpose of this thesis is detecting spam emails.... 

Nowadays spam has become as a universal problem which all email users are familiar with it. Studies show that a large proportion of sent emails are spam. Obviously it results in wasting a vast range of resources. There is different ways to fight spam; each of them has its own strengths and weaknesses. The most common filtering technique is content based filtering. This problem has been addressed as a text classification problem. Two main defect of spam filtering techniques are manually definition of rules and circumventing them, one solution for overcoming this problem is applying machine learning algorithms. Spam classification using machine learning techniques is very successful and... 

What people write about their experience on web pages or social media about a product they have used or a service they have received can influence the reputation and the popularity of a certain brand with a great deal. If the reviews that exist about a product or a service of a certain company are mainly positive, it can increase the profit and improve the image of the company. On the other hand, mostly negative reviews can decrease the profit and destroy a company's image irreversibly. Unfortunately, because of this great influence that online reviews have over general public's decision to use a a product or a service of a brand, some companies hire people to write undeserving positive...