Browsing by Author "Olabiyisi Stephen O."

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    Implications of System Identification Techniques on ANFIS E-learners Activities Models-A Comparative Study
    (Transactional on Machine Learning and Artificial Intelligence, 2016-02-21) Isiaka, Mope Rafiu; Omidiora Elijah Olusayo; Olabiyisi Stephen O.; Okediran Oladotun O.; Babatunde Ronke Seyi
    Efficient e-learners activities model is essential for real time identifications and adaptive responses. Determining the most effective Neuro- Fuzzy model amidst plethora of techniques for structure and parameter identifications is a challenge. This paper illustrates the implication of system identification techniques on the performance of Adaptive Network based Fuzzy Inference System (ANFIS) E-learners Activities models. Expert knowledge and Historical data were used to formulate the system and their performances were compared. Similarly, comparison was made between memberships functions selected for Historical data identification. The efficiencies of the simulated models in MATLAB editor were determined using both classification uncertainty metrics and confusion matrix–based metrics. The classification uncertainty metrics considered are Mean Absolute Error (MAE) and Root Mean-Squared Error (RMSE). The confusion matrix-based metrics used are Accuracy, Precision and Recall. It was discovered that the model based on Experts Knowledge after training outperformed those based on Historical Data. The performances of the Membership Functions after ranking are Sigmoid, Gaussian, Triangular and G-Bells respectively.
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    Mamdani Fuzzy Model for Learning Activities Evaluation
    (International Journal of Applied Information Systems (IJAIS), 2014-03-05) Isiaka Rafiu M.; Omidiora Elijah O.; Olabiyisi Stephen O.
    The intent of this paper is to determine the extent to which fuzzy model could suitably modelled learner activities in E learning system. However, the paucity of public dataset that meet the exact requirement of this work poses challenges, which necessitate dataset simulation. The detail approach used for the dataset simulation and the fuzzy model were discussed. Construction of the Inference Mechanism using the Relational Calculus and Mamdani approaches were demonstrated. The performance of the simulated model in MATLAB was measured using classifier uncertainty and confusion based metrics. The Mean Absolute Error (MAE) is 10.45; Root Mean Square Error (RMSE) is 8.71. The result shows that Fuzzy logic (White-Box Model) has a low classification error and invariably a higher accuracy for estimating learner activities. Subsequently, the result obtained shall be revalidated using live data of students’ activities in an online course. Furthermore, the current Mamdani’s model performance shall be compared with its equivalent Neuro_Fuzzy Model. The more efficient of the two models shall be the choice for integration into an Open Source Learning Management System for automatic learning activities evaluation.