Browsing by Author "Abdul-Hadi, J."

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    COMPARING THE PERFORMANCE OF PREDICTIVE MODELS CONSTRUCTED USING THE TECHNIQUES OF FEED-FORWARD AND GENERALIZED REGRESSION NEURAL NETWORKS
    (Universiti Malaysia Pahang, 2016-02) Ajiboye, A.R.; Abdullah-Arshah, R.; Honqwu, Q.; Abdul-Hadi, J.
    Construction of predictive model is primarily aimed at using the known attributes to determine the present or the future unknown attributes for efficient planning and decision making. The accuracy of predictive model is therefore, paramount to achieving network outputs that are well correlated with the known or target output. In this paper, two predictive models are constructed using the techniques of feed-forward and generalized regression neural networks. Experiments are conducted with a Matlab software and the performance of the two models is evaluated for accuracy. Their simulated outputs are compared to determine their response to untrained data. Findings from this study show that, the generalized regression neural network consistently shows a more accurate result. The Mean Absolute Error computed for the two models also reveals that, feed-forward neural network records higher error value.
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    An Improved Technique for the Removal and Replacement of the Inconsistencies in Numeric Dataset
    (IEEE Nigeria Chapter., 2015-05) Abdul-Hadi, J.; Ajiboye, A.R.; Abba, A.
    The task of ensuring the removal of anomalies in an unclean numeric dataset, with a view to putting the data in a suitable format for exploration purposes is a major phase in the data mining process. In the process of exploring an unclean numeric dataset to unveil their useful patterns or structure, a thorough pre-processing task is inevitable in order to achieve a noise-free dataset. Poor quality data can be misleading if analysed or used to build models, hence, there is need to remove discrepancies that may be present in the data prior to exploring them. In this paper, a cleaning algorithm is proposed and implemented in order to remove the inconsistencies in a numeric dataset. The implementation of the proposed algorithm uses the Java language and the resulting outputs reveal the efficiency of the proposed approach. In order to evaluate the effectiveness of the proposed algorithm, it is compared to one of the existing methods based on some metrics. The comparisons show that, the proposed technique is efficient and can be used as an alternative technique for the removal of outliers in numeric data. This approach is also found to be reliable as it consistently gives an accurate output that is free of outliers.