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Mergani A. Khairalla; Mohammed A.A. Al Qaness; Xu Ning; Nashat T. Al Jallad. Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features. International Journal of Management in Education 2019, 13, 97 .
AMA StyleMergani A. Khairalla, Mohammed A.A. Al Qaness, Xu Ning, Nashat T. Al Jallad. Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features. International Journal of Management in Education. 2019; 13 (2):97.
Chicago/Turabian StyleMergani A. Khairalla; Mohammed A.A. Al Qaness; Xu Ning; Nashat T. Al Jallad. 2019. "Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features." International Journal of Management in Education 13, no. 2: 97.
Predicting students' dropout/stop-out and switch registration aspects at college is one of the important managerial issues that concern the academic institutions. This issue presents a specific challenge due to a large number of factors that can affect the student's decision and the imbalanced nature of the educational data. In this paper, a novel feature extraction method is applied to student satisfaction and socio-economic features during the pre-processing stage to reduce the high dimensionality of the data. Thus, different interpretable data mining approaches, including decision trees and rule induction methods, were examined using actual data of students at the Technical University of Palestine. After resolving imbalanced problem of the students' data, the results showed that the student satisfaction and socio-economic status predictors are important to distinguish different registration aspects. Moreover, the results revealed that J4.8 algorithm achieved best results due to the ability to apply an appropriate trade-off regarding accuracy versus interpretability.
Nashat T. Al Jallad; Xu Ning; Mergani Khairalla; Mohammed A.A. Al Qaness. Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features. International Journal of Management in Education 2019, 13, 97 .
AMA StyleNashat T. Al Jallad, Xu Ning, Mergani Khairalla, Mohammed A.A. Al Qaness. Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features. International Journal of Management in Education. 2019; 13 (2):97.
Chicago/Turabian StyleNashat T. Al Jallad; Xu Ning; Mergani Khairalla; Mohammed A.A. Al Qaness. 2019. "Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features." International Journal of Management in Education 13, no. 2: 97.
In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investigates the Stacking Multi-Learning Ensemble (SMLE) model for time series prediction problem over various horizons with a focus on the forecasts accuracy, directions hit-rate, and the average growth rate of total oil demand. This investigation presents a flexible ensemble framework in light of blend heterogeneous models for demonstrating and forecasting nonlinear time series. The proposed SMLE model combines support vector regression (SVR), backpropagation neural network (BPNN), and linear regression (LR) learners, the ensemble architecture consists of four phases: generation, pruning, integration, and ensemble prediction task. We have conducted an empirical study to evaluate and compare the performance of SMLE using Global Oil Consumption (GOC). Thus, the assessment of the proposed model was conducted at single and multistep horizon prediction using unique benchmark techniques. The final results reveal that the proposed SMLE model outperforms all the other benchmark methods listed in this study at various levels such as error rate, similarity, and directional accuracy by 0.74%, 0.020%, and 91.24%, respectively. Therefore, this study demonstrates that the ensemble model is an extremely encouraging methodology for complex time series forecasting.
Mergani A. Khairalla; Xu Ning; Nashat T. Al-Jallad; Musaab O. El-Faroug. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. Energies 2018, 11, 1605 .
AMA StyleMergani A. Khairalla, Xu Ning, Nashat T. Al-Jallad, Musaab O. El-Faroug. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model. Energies. 2018; 11 (6):1605.
Chicago/Turabian StyleMergani A. Khairalla; Xu Ning; Nashat T. Al-Jallad; Musaab O. El-Faroug. 2018. "Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model." Energies 11, no. 6: 1605.
Nashat T . Aljallad; Xu Xing; Mergani A. Khairalla; Mohammed A. A. Al-Qaness. A Novel Data Classification Approach Using Fuzzy Logic. International Symposium on Computer Science and Artificial Intelligence(ISCSAI) 2017, 1 .
AMA StyleNashat T . Aljallad, Xu Xing, Mergani A. Khairalla, Mohammed A. A. Al-Qaness. A Novel Data Classification Approach Using Fuzzy Logic. International Symposium on Computer Science and Artificial Intelligence(ISCSAI). 2017; ():1.
Chicago/Turabian StyleNashat T . Aljallad; Xu Xing; Mergani A. Khairalla; Mohammed A. A. Al-Qaness. 2017. "A Novel Data Classification Approach Using Fuzzy Logic." International Symposium on Computer Science and Artificial Intelligence(ISCSAI) , no. : 1.
Mergani A. Khairalla; Xu Ning. Financial Time Series Forecasting Using Hybridized Support Vector Machines and ARIMA Models. Proceedings of the 2017 International Conference on Software and System Process 2017, 94 -98.
AMA StyleMergani A. Khairalla, Xu Ning. Financial Time Series Forecasting Using Hybridized Support Vector Machines and ARIMA Models. Proceedings of the 2017 International Conference on Software and System Process. 2017; ():94-98.
Chicago/Turabian StyleMergani A. Khairalla; Xu Ning. 2017. "Financial Time Series Forecasting Using Hybridized Support Vector Machines and ARIMA Models." Proceedings of the 2017 International Conference on Software and System Process , no. : 94-98.