Azman Ta'a

Mobile network quality of experience using big data analytics approach

Traditionally, Quality of experience is mostly examined in a laboratory experiments to enable a fixed contextual factor. While the results present an estimated mean opinion score representing perceived QoE. It is imperative to estimate mean opinion score employing large data (big data) gathered from the mobile network comprising of different users location and time for a specific service. Because time and location can have a huge influence on the user perceived quality of experience. Therefore, this paper proposed a framework for modelling perceived QoE through big data analytics. The proposed framework describes the process of estimating perceived quality of experience to assist the mobile network operators effectively manage the network performance and aid satisfactory provision of mobile internet services.

Themes-based classification for Al-Quran knowledge ontology

Al-Quran knowledge representations involved classification of Al-Quran verses for providing better understanding of the readers. In the current era of social media challenges, the representation of knowledge must be understood by human and computer in order to ensure the correctness of Al-Quran semantics are persevered. Current approaches used conventional methods such as taxonomy, hierarchy or tree structure, which only provides a concept definition without linked to other sources of knowledge explanation. This research aims to develop the Al-Quran Ontology by using theme-based classification approach. The ontology model for Al-Quran is developed based on the Al-Quran knowledge theme defined in Syammil Al-Quran Miracle the Reference. The theme-based ontology approach has shown that the Al-Quran knowledge can be classified and presented systematically. This will encourage the development of applications for Al-Quran readers. Moreover, the ontology structure that representing the theme concepts in Al-Quran was reviewed and validated by the domain experts in Al-Quran knowledge.