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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Salwa Khalid Abdulateef
Tikrit University

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Prediction of student satisfaction on mobile-learning by using fast learning network Laman Radi Sultan; Salwa Khalid Abdulateef; Bushra Abdullah Shtyat
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp488-495

Abstract

The rapid advancement of mobile technologies over the past decade has had a significant impact on the appearance of M-learning applications. The research proposes the fast learning network model to investigate and identify the factors that affect student satisfaction in M-learning for the University of Tikrit students. The research model is conducted utilizing a questionnaire of 300 participating students based on variables. This research showed that the proposed model's perfor mance was superior to artificial neural network, k-nearest neighbors, and multilayer perceptron algorithms. The accuracy and specificity of predicting the student satisfaction coefficients in M-learning were 91.6% and 92.85%, respectively. The proposed findings demonstrate that diversity in the evaluation, teacher attitude and response, and quality of technology are key operators of student satisfaction.
An optimise ELM by league championship algorithm based on food images Salwa Khalid Abdulateef; Taj-Aldeen Naser Abdali; Mohanad Dawood Salman Alroomi; Mohamed Aktham Ahmed Altaha
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp132-137

Abstract

This paper presents an optimisation of extreme learning machine by league championship algorithm based on food images. extreme learning machine (ELM) is an effective classifier because of the performance which is higher than other classifiers’ aspects. However, some important drawbacks still work as a hindrance like failure of optimal selection weights for the weights of the input-hidden layer and the output of the threshold. In spite of the wide number of problem-solving attempts, there was no solution to be considered effective. This paper presents the approach of hybrid learning and the League Championship Algorithm is used by for the purpose of selecting the input weights and the thresholds outputs. The experimental outcomes showed that the performance of proposed technique is superior as compared according to different scenarios of the measures to benchmark. The proposed method has achieved an overall accuracy of 95% for UEC food 100 dataset and 94% for UEC food 256 dataset comparing with 94% and 80% for baseline approaches.
Co-Authors Bushra Abdullah Shtyat Laman Radi Sultan Mohamed Aktham Ahmed Altaha Mohanad Dawood Salman Alroomi Taj-Aldeen Naser Abdali