CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
Reseach Article

A Prediction Method to Improve Training Management

by Mohamed Elglaad, A. A. Ewees, E. E. Abd-Elrazek
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 4
Year of Publication: 2017
Authors: Mohamed Elglaad, A. A. Ewees, E. E. Abd-Elrazek
10.5120/ijca2017914033

Mohamed Elglaad, A. A. Ewees, E. E. Abd-Elrazek . A Prediction Method to Improve Training Management. International Journal of Computer Applications. 166, 4 ( May 2017), 39-43. DOI=10.5120/ijca2017914033

@article{ 10.5120/ijca2017914033,
author = { Mohamed Elglaad, A. A. Ewees, 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 = {9},
url = { https://ijcaonline.org/archives/volume166/number4/27661-2017914033/ },
doi = { 10.5120/ijca2017914033 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:50.519898+05:30
%A Mohamed Elglaad
%A A. A. Ewees
%A E. E. Abd-Elrazek
%T A Prediction Method to Improve Training Management
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 4
%P 39-43
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. Bahrani, M. (2014). Training Needs of Faculty Members in Applied Sciences Colleges in the Sultanate of Oman, not published, Faculty of Science and Arts, University of Nizwa
  2. Kfoury, Mohamed Ahmed Kamal Ibrahim. (2015). Construction of a proposed expert to specialist educational technology system in the light of professional competencies, requirements and functional effectiveness in meeting the future training needs. Ph.D thesis, not published, Faculty of Education, Helwan University.
  3. Ghuraibi, Afaf Abdullah. (2009). Development of a training system during the service in the light of technological development requirements: prospective study. Ph.D thesis, not published, Faculty of Education in Damietta, Mansoura University.
  4. Qouta, Marwa (2008). Teacher in-service training in Damietta in the light of the requirements of the era of globalization. Master thesis, not published, Faculty of Education, Damietta University.
  5. Siraj, Ragab Abdullah (2010), The reality of the process of determining for workers in nongovernmental organizations training needs, Master thesis, Azhar Gaza University, Faculty of Economics and Administrative Sciences.
  6. Troanh, Tahseen Ahmed. (2011). Identify training needs as a basis for the planning process in the security services. Seminar modern methods of planning and training (both theory and practical) in the security services. Graduate School, University of Nayef.
  7. Ahmed, Mohammad Mohammadi Hassania. (2009). Establishment a system to help students choose the physical components harmonized to assemble the computer. Master thesis, not published, Faculty of Qualitative Education, Mansoura University.
  8. Abdullah, Khababah and Abdul Wahab, Jabari. (2010). Expert systems and decision support systems as input to decision-making in the organization, available on: Islamic Economics & Finance Pedia, accessed online (13-04-2017) at “http://iefpedia.com/arab/wp-content/uploads/2010/03/"
  9. Bdrany TH., Sailo R. (2014). Evaluation of time series prediction of temperature rates using neural networks. Iraqi Journal of Statistical Sciences, Issue (26), Iraq.
  10. Elglaad, M., A. A. Ewees, E. E. Abd ElRazik, and M. M. Refaat (2016). Computational Comparison of Prediction Algorithms for Predicting Employees Training Needs. IJRIT International Journal of Research in Information Technology, Vol. 4(10), pp. 94-101
  11. Breiman, L. (1996). Bagging predictors. Machine learning 24: 123.
  12. Dimitoglou, G., James, A. A, & Carol M. J. (2012). Comparison of the C4.5 and a Naive Bayes Classifier for the Prediction of Lung Cancer Survivability. Journal of Computing, Vol. 4 (8).
  13. Seongwook Youn, Dennis McLeod,2007. A Comparative Study for Email Classification, University of Southern California, Los Angeles, CA 90089, USA.
  14. Margaret H. Danham, S. Sridhar, Data mining, Introductory and Advanced Topics, Person education, 1st ed., 2006.
  15. Demuth, H., & Beale, M. (2009). Matlab neural network toolbox user’s guide version 6. The MathWorks Inc.
  16. Rosenblatt, F. (1961). Principles of neurodynamics. perceptrons and the theory of brain mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc Buffalo Ny.
  17. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation (No. ICS-8506). California Univ San Diego La Jolla Inst for Cognitive Science.
  18. Cybenko, G., (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, Vol. 2 (4), 303–314 . Springer-Verlag New York Inc.
  19. Nguyen, D., & Widrow, B. (1990). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (pp. 21-26). IEEE.
  20. Chai, Tianfeng, and Roland R. Draxler. Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development Discussions 7 (2014): 1525-1534.
  21. Ghose, A., Governatori, G. and Sadnanda, R. (2009). Agent Computing and Multi-Agent Systems: 10th Pacific Rim International conference on Multi-agent PRIMA 2007, Springer village Berlin.
Index Terms

Computer Science
Information Sciences

Keywords

Bagging NaviaBayes Neural Network Predicting Training Needs.