Deep neural networks for spam classification
Kasmani, Mohamed Khizer (2013) Deep neural networks for spam classification. [Dissertation (University of Nottingham only)]
This project elucidates the development of a spam filtering method using deep neural networks. A classification model employing algorithms such as Error Back Propagation (EBP) and Restricted Boltzmann Machines (RBM) is used to identify spam and non-spam emails. Moreover, a spam classification system employing deep neural network algorithms is developed, which has been tested on Enron email dataset in order to help users manage large volumes of email and, furthermore, their email folders. The sample size of the data used for this study -- collected from Enron business users – comprises 158 users and 200,399 emails at an average of 757 emails per user. It has been observed that most users use folders to classify their emails, with some employing a fewer numbers of folders than others.
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