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Title: A k-ELM approach to the prediction of number of students taking make-up exams
Other Titles: Bütünleme sınavına girecek öğrenci sayısının tahmini için k-ELM yaklaşımı
Authors: Kıran, Mustafa Servet
Sıramkaya, Eyup
Esme, Engin
Keywords: Extreme Learning Machine
Multiple Extreme Learning Machine
Artificial Bee Colony
Make-Up Exam
Extreme Learning-Machine
Publisher: Gazi Univ, Fac Engineering Architecture
Abstract: Purpose: The main objective of this study is to present a novel problem, and novel methodology to solve this problem. The problem is to predict the number of students who fail the course and will join the make-up exams. Theory and Methods: The number of students who fail the course should take a make-up exam, but some of them do not join these exams due to internal or external motivations, and this causes waste of resources. Majority of voting-based extreme learning machines have been proposed to solve the problem, and the ELM parameters have been optimized by artificial bee colony algorithm. Results: The proposed approach shows better performance than the extreme learning machines in terms of classification accuracy. Conclusion: Before the scheduling make-up exams, the number of students who will join the exams should be predicted by the proposed or similar approaches in order to use resources efficiently.
ISSN: 1300-1884
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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