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An Electronic Registration for Undergraduate Students with Department Selection Based on Artificial Neural Network | ||
| Kirkuk Journal of Science | ||
| Article 1, Volume 13, Issue 1, 2018, Pages 273-288 PDF (0 K) | ||
| DOI: 10.32894/kujss.2018.143044 | ||
| Author | ||
| Banaz Anwer Qader | ||
| Abstract | ||
| The objective of the present research is to facilitate the administrative procedures associated with student registration process, and to ensure equal opportunities for all applicants in college. It aims to assist students in identifying appropriate alternatives available to the departments electronically anytime and anywhere saving time and effort for the student. For this purpose, an intelligent e-government system named "An Electronic Intelligent Registration with Department Selection" (E-IRDS) is designed as one of the intelligent e- services in Iraqi e-governance by using many tools and programming languages which are (PHP, MYSQL, HTML, CSS, XML, NOTPAD++, C#). Artificial neural networks (ANNs) technology is applied, notably Kohonen's self-organizing map (SOM) as one of the important unsupervised classification algorithms of machine learning for classifying and distributing the students automatically into the college academic departments based on their desires, their total degrees, and according to scientific plan for each department, in addition to the specific and personal student information. The applied results based on international standards demonstrated the accuracy of Kohonen's SOM algorithm in classification and distribution methods at least time and possible learning ratio. The system test and assessment results confirmed that it is characterized with a very high security and reliability and accuracy. It is also distinguished with very high efficiency and transparency as well as flexibility and high performance speed. The results also emphasized the ease and availability of the system to all students, besides the possibility of troubleshot and correct errors easily. | ||
| Keywords | ||
| government; Kirkuk; registration; unsupervised machine learning; Kohonen; organizing map | ||
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