Odaro Osayande

Work place: Centre for Learning Resources, Covenant University, Ota, Ogun State, Nigeria

E-mail: odaro.osayande@covenantuniversity.edu.ng

Website: https://orcid.org/0000-0002-5771-9548

Research Interests: Information Security, Digital Library, Information-Theoretic Security


Odaro Osayande, Ph.D. is a professional Librarian and an information services Manager. He bagged his Ph.D. and M.L.I.S (Master, Library and Information Studies in 2020 and 2010 from the University of KwaZulu- Natal, Pietermaritsburg campus, South Africa, and the prestigious University of Ibadan respectively. He got his BSc. Ed. In Library Science and Political. He is the author of several peer-reviewed local and international journal articles and conference proceedings. His research areas include library security, library administration, library induction ICT in library services, etc. His email is: odaro.osayande@covenantuniversity.edu.ng

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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Optimised MLP Neural Network Model for Optimum Prognostic Learning of out of School Children Trend in Africa: Implication for Guidance and Counselling

By Edith Edimo Joseph Joseph Isabona Odaro Osayande Ikechi Irisi

DOI: https://doi.org/10.5815/ijmecs.2023.01.01, Pub. Date: 8 Feb. 2023

One crucial and intricate problem in the education sector that must be dealt with is children who initially enrolled in schools but later dropped out before finishing mandatory primary education. These children are generally referred to as out-of-school children. To contribute to the discuss, this paper presents the development of a robust Multilayer Perceptron (MLP) based Neural Network Model (NN) for optimal prognostic learning of out-of-school children trends in Africa. First, the Bayesian optimization algorithm has been engaged to determine the best MLP hyperparameters and their specific training values. Secondly, MLP-tuned hyperparameters were employed for optimal prognostic learning of different out-of-school children data trends in Africa. Thirdly, to assess the proposed MLP-NN model's prognostic performance, two error metrics were utilized, which are the Correlation coefficient (R) and Normalized root means square error (NRMSE). Among other things, a higher R and lower NRMSE values indicate a better MLP-NN precision performance. The all-inclusive results of the developed MLP-NN model indicate a satisfactory prediction capacity, attaining low NRMSE values between 0.017 - 0.310 during training and 0.034 - 0.233 during testing, respectively. In terms of correlation fits, the out-of-school children's data and the ones obtained with the developed MLP-NN model recorded high correlation precision training/testing performance values of 0.9968/0.9974, 0.9801/0.9373, 0.9977/0.9948 and 0.9957/0.9970, respectively. Thus, the MLP-NN model has made it possible to reliably predict the different patterns and trends rate of out-of-school children in Africa. One of the implications for counselling, among others, is that if every African government is seriously committed to funding education at the foundation level, there would be a reduction in the number of out-of-school children as observed in the out-of-school children data.

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