Okiemute Roberts Omasheye

Work place: Delta State College of Education, Mosogar, Nigeria

E-mail: okiemuteomasheye@yahoo.com


Research Interests: Image and Sound Processing, Program Analysis and Transformation


Okiemute Roberts Omasheye is a senior lecturer at the Delta State College of Education, Mosogar, Nigeria. He received a B.Sc and M.Sc degrees in Industrial Physics and Communication Electronics in 2006 and 2010 respectively. He is currently persuing a Ph.D degree in Communication Electronics. He has published several articles in reputable national and international journals and has also attended several conferences in the area of wireless communication systems. He specializes in signal loss modelling and coverage analysis. He can be reached via okiemuteomasheye@yahoo.com.

Author Articles
Statistical and Machine Learning Approach for Robust Assessment Modelling of Out-of-School Children Rate: Global Perspective

By Edith Edimo Joseph Joseph Isabona Sunday Dare Odaro Osayande Okiemute Roberts Omasheye

DOI: https://doi.org/10.5815/ijitcs.2023.03.01, Pub. Date: 8 Jun. 2023

The negative impact of out-of-school students' problems at the basic and high-school levels is always very weighty on the affected individuals, parents, and society at large. Owing to the weighty negative consequences, policymakers, different government agencies, educators and researchers have long been looking for how to effectively study and forecast the trends as a means of offering a concrete solution to the problem. This paper develops a better hybrid machine learning method, which combines the least square and support vector machine (LS-SVM) model for robust prediction improvement of out-of-school children trend patterns. Particularly, while other previous works only engaged some regional and few samples of out-of-school datasets, this paper focused on long-ranged global out-of-school datasets, collated by UNESCO between 1975- 2020. The proposed hybrid method exhibits the optimal precision accuracies with the LS-SVM model in comparison with ones made using the ordinary SVM model. The precision performance of both LS-SVM and SVM was quantified and a lower NRMSE value is preferred. From the results, the LS-SVM attained lower error values of 0.0164, 0.0221, 0.0268, 0.0209, 0.0158, 0.0201, 0.0147 and 0.0095 0.0188, compared to the SVM model that attained higher NRMSE values of 0.041, ,0.0628, 0.0381, 0.0490, 0.0501, 0.0493, 0.0514, 0.0617 and 0.0646, respectively. By engaging the MAPE indicator, which expresses the mean disconnection between the sourced and predicted values of the out-of-school data. By means of the MAPE, LS-SVM attained lower error values of 0.51, 1.88, 0.82, 2.38, 0.62, 2.55, 0.60, 0.60, 1.63 while SVM attained 1.83, 7.39, 1.79 7.01, 2.43, 8.79, 2.58, 4.13, 6.18. This implies that the LS-SVM model has better precision performance than the SVM model. The results attained in this work can serve as an excellent guide on how to explore hybrid machine-learning techniques to effectively study and predict out-of-school students among researchers and educators.

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