Work place: IT Operations and Systems Manager at CACBank®, Open University Malaysia (OUM) – UST Centre, Sana’a, Yemen
E-mail: yahya.alamlahi@hotmail.com
Website:
Research Interests: Artificial Intelligence, Natural Language Processing, Neural Networks, Network Architecture, Network Security, Data Structures and Algorithms
Biography
Yahya M. Alamlahi received his B.Sc. in Computer Science from Sana’a University, Yemen, and Master of Information Technology from Open University Malaysia, UST Center. His research area includes natural language processing, neural network and artificial intelligence in general. He is ITIL certified and a certified engineer in storage and virtualization from EMC and VMware (EMCSAe, VCP). Currently, he is IT Operations and Systems manager at CACBank® in Yemen.
By Yahya Alamlahi Abdulrahman Muthana
DOI: https://doi.org/10.5815/ijcnis.2018.12.01, Pub. Date: 8 Dec. 2018
This research proposes a model for presenting email to Artificial Neural Network (ANN) to classify spam and legitimate emails. The proposed model based on selecting wise 13 fixed features relevant to spam emails combined with text features.
The experiment tests many scenarios to find out the best-suited combination of features representation. These scenarios show the effect of using term frequency (tf), term frequency-inverse document frequency (tf*idf), Level two (L2) normalization, and principal component analysis (PCA) for dimension reduction. Text features vectors are represented in the principal component space as a reduced form of the original features vectors. PCA reduction effect on ANN performance is also studied.
Among these tests, best-suited model that improves ANN classification and speeds up training is concluded and suggested. An idea of integrating ANN anti-spam filter into score-based anti-spam systems is also explained in this paper. XEAMS email gateway, the commercial anti-spam, already uses Na?ve Bayes (NB) filter as one of its many techniques to identify spam email. The proposed approach influences filtering results by 7.5% closer to XEAMS anti-spam system results than NB filter does on real-life emails of Arabic and English messages.
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