Data Mining Based Universities ESecure Student Admission System

Data Mining Based Universities E-Secure Student Admission System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2019 by IJETT Journal
Volume-67 Issue-2
Year of Publication : 2019
Authors : Ahmed Shitaya, Reda Abo-Al-Ez, Ahmed Mohamed Al-Mahdy
DOI :  10.14445/22315381/IJETT-V67I2P208

Citation 

MLA Style: Ahmed Shitaya, Reda Abo-Al-Ez, Ahmed Mohamed Al-Mahdy "Data Mining Based Universities E-Secure Student Admission System" International Journal of Engineering Trends and Technology 67.2 (2019): 37-40.

APA Style:Ahmed Shitaya, Reda Abo-Al-Ez, Ahmed Mohamed Al-Mahdy (2019). Data Mining Based Universities E-Secure Student Admission System. International Journal of Engineering Trends and Technology, 67(2), 37-40.

Abstract
The process of joining students in universities is a very important and complex process, where the selection of the best students applying to universities is an important element of the advancement of the educational process and training. Develop an Electronic Secure System to Admission Students in University (E-SSASU) is a new vision for university admission systems by building a security electronic system that is an alternative to the existing traditional admission systems used by universities. This system is able to overcome many of the obstacles and challenges faced by universities in the enrollment of students. The main idea of the research begins by checking the student`s data with national identification number and national data to select their qualifications, then distributing the student due to the university`s test committees in accordance with the general secondary certificate obtained. Conducting initial tests on students then using data mining methods to predict the performance of student. The system result shows that data mining algorithms prediction success rate is 87%.

Reference
[1] Tr?ek, D., Trobec, R., Paveši?, N., & Tasi?, J. F. (2007). Information systems security and human behaviour. Behaviour & Information Technology, 26(2), 113-118.
[2] Fong, S., & Biuk-Aghai, R. P. (2009). An automated university admission recommender system for secondary school students. In The 6th International Conference on Information Technology and Applications (p. 42).
[3] Baradwaj, B. K., & Pal, S. (2012). Mining educational data to analyze students` performance. arXiv preprint arXiv:1201.3417.
[4] Kabakchieva, D. (2012). Student performance prediction by using data mining classification algorithms. International Journal of Computer Science and Management Research, 1(4), 686-690.
[5] Amare S, Yirgalem T, Tesfamaryam T, Samirawit A, Dagimawit G, Mentesinot T, T.Prince "Centralized School Management System for Government Schools in Ethiopia using Distributed Database", International Journal of Engineering Trends and Technology (IJETT), V60(2),97-101 June 2018.
[6] Akhmad, M., Indra, W., Akbar, N. S., & Inge, H. (2019). Application New Employee Recruitment and Placement. International Journal of Computer Trends and Technology (IJCTT), 1-5.
[7] Tegegne, A. K., & Alemu, T. A. (2018). Educational data mining for students’ academic performance analysis in selected Ethiopian universities. Information Impact: Journal of Information and Knowledge Management, 9(2), 1-15.
[8] Isha, S., & Dinesh, K. (2018). Predicting Student Performance Using Classification Data Mining Techniques. International Journal of Computer Sciences and Engineering, 1-6.
[9] Heena, S., Mayur, M., Prashant, K., & Prof. Surekha, J. (2018). Prediction of Student Enrolment Using Data Mining Techniques. International Research Journal of Engineering and Technology (IRJET), 1-4.
[10] Ibrahim, E. A. (2018). Using a statistical model to predict student success in Texas Woman`s University mathematics program (Doctoral dissertation).
[11] Stephens, R. (2015). Beginning software engineering. John Wiley & Sons.
[12] Refaat, Y. A. A., & Ahmed, A. A. (2004). Introduction to Graphical User Interface (GUI) MATLAB 6.5. IEEE UAEU Student Branch UAE University, 2-35.
[13] Okubo, F., Yamashita, T., Shimada, A., & Ogata, H. (2017, March). A neural network approach for students` performance prediction. In LAK (pp. 598-599).

Keywords
Electronic secure system, educational data mining, neural network algorithms, NNC.