Ayisat Asaju-Gbolagade
@uilugportal.unilorin.edu.ng
,
,
University of Ilorin
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Science, Artificial Intelligence, Computer Science Applications
8
Scopus Publications
Scopus Publications
- An overview of enhancing encryption standards for multimedia in explainable artificial intelligence using residue number systems for security
- Implementation of quality of experience prediction framework through mobile network data
- Towards real-time customer satisfaction prediction model for mobile internet networks
Ayisat W. Yusuf-Asaju, Zulkhairi B. Dahalin, and Azman Ta’a
Springer International Publishing - Framework for modelling mobile network quality of experience through big data analytics approach
Ayisat Wuraola Yusuf-Asaju, Zulkhairi Md Dahalin, and Azman Ta’a
UUM Press, Universiti Utara Malaysia - Implementation of quality of experience prediction framework through mobile network data
- Framework For Modelling Mobile Network Quality Of Experience Through Big Data Analytics Approach
Ayisat Wuraola Yusuf-Asaju, Zulkhairi Md Dahalin, and Azman Ta’a
UUM Press, Universiti Utara Malaysia
The increase in the usage of different mobile internet applications can cause deterioration in the mobile network performance. Such deterioration often declines the performance of the mobile network services that can influence the mobile Internet user’s experience, which can make the internet users switch between different mobile network operators to get good user experience. In this case, the success of mobile network operators primarily depends on the ability to ensure good quality of experience (QoE), which is a measure of users’ perceived quality of mobile Internet service. Traditionally, QoE is usually examined in laboratory experiments to enable a fixed contextual factor among the participants even though the results derived from these laboratory experiments presented an estimated mean opinion score representing perceived QoE. The use of user experience dataset involving time and location gathered from the mobile network traffic for modelling perceived QoE is still limited in the literature. The mobile Internet user experience dataset involving the time and location constituted in the mobile network can be used by the mobile network operators to make data-driven decisions to deal with disruptions observed in the network performance and provide an optimal solution based on the insights derived from the user experience data. Therefore, this paper proposed a framework for modelling mobile network QoE using the big data analytics approach. The proposed framework describes the process of estimating or predicting perceived QoE based on the datasets obtained or gathered from the mobile network to enable the mobile network operators effectively to manage the network performance and provide the users a satisfactory mobile Internet QoE. - Mobile network quality of experience using big data analytics approach
Ayisat W. Yusuf-Asaju, Zulkhairi B. Dahalin, and Azman Ta'a
IEEE
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 user's 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. - A proposed analytical customer satisfaction prediction model for mobile internet networks