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Mahmoud M. Abu Ghosh,Rasha R. Atallah,Samy S. Abu Naser 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.2
Many Palestinian higher education institutions had a successful experience in utilizing Electronic Services and Electronic learning, such as all the academic services for student and lecturer, schedule lectures, exams schedule, viewing the academic information, grades report, printing transcript, registering courses, library services, Email services, News and Announcements, but these services are supported by web based applications or desktop application which restrict the access of the users using computers or laptops, But there is a lack in supporting these services with mobile. Especially when dealing with sensitive data as inserting grades for courses. So in this paper we present a system called Secure Mobile Cloud Computing for Sensitive Data: Teacher Services for Palestinian Higher Education Institutions (MCCTSs) which is a mobile application to facilities access using RSA algorithm to encrypt the data which sent and received through Cloud computing application. MCCTSs serve the lecturers of Palestinian higher education institutions. Agile methodology was adapted to develop MCCTSs application. MCCTSs users successfully insert the grades securely and encrypted any time anywhere using RSA algorithm. It is hoped that the result of this study will encourage the universities to engage MCCTSs in their services. From the results we obtained, it is proved that RSA provides protection for the data, which is stored in Cloud. Only authorized user can read the encrypted data and decrypt it. Even if anyone happens to read the data accidentally, the original meaning of the data will not be understood.
Samy Abu Naser,Ihab Zaqout,Mahmoud Abu Ghosh,Rasha Atallah,Eman Alajrami 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2
In this paper an Artificial Neural Network (ANN) model, for predicting the performance of a sophomore student enrolled in engineering majors in the Faculty of Engineering and Information Technology in Al- Azhar University of Gaza was developed and tested. A number of factors that may possibly influence the performance of a student were outlined. Such factors as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during the student freshman year, number of credits passed, student cumulative grade point average of freshman year, types of high school attended and gender, among others, were then used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was developed and trained using data spanning five generations of graduates from the Engineering Department of the Al- Azhar University, Gaza. Test data evaluation shows that the ANN model is able to correctly predict the performance of more than 80% of prospective students.