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A Prediction Method to Improve Training Management

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Mohamed Elglaad, A. A. Ewees, E. E. Abd-Elrazek

Mohamed Elglaad, A A Ewees and E E Abd-Elrazek. A Prediction Method to Improve Training Management. International Journal of Computer Applications 166(4):39-43, May 2017. BibTeX

	author = {Mohamed Elglaad and A. A. Ewees and E. E. Abd-Elrazek},
	title = {A Prediction Method to Improve Training Management},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {166},
	number = {4},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {39-43},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017914033},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The current research aims to use expert systems techniques to predict the training needs of trainees based on several factors related to the functional status of each employee (group, quality, job, training courses), which are essential factors in this forecasting process; because of the diversity of the training needs in light of the job conditions, technological and international development. So, the hold makers are imposed to identify these needs which are determined as the most important processes lead to success the training process. In this paper, three prediction algorithms were used: Bagging, NaviaBayes, and Neural Network to predict the training needs of the trainees in order to support and decision-making among the decision makers in education and increase the accuracy as well as the effectiveness of the training courses. The dataset consisted of 334 cases. The results of the experiments showed that the Bagging algorithm achieved the better accuracy against the rest of the algorithms.


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Bagging; NaviaBayes; Neural Network; Predicting Training Needs.