Vol.8, No.4, November 2019.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Management Information System for Predicting Quantity Martials


Boumedyen Shannaq, Ibrahim Al Shamsi, Saif Nabhan Abdul Majeed


© 2019 Boumedyen Shannaq, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 8, Issue 4, Pages 1143-1149, ISSN 2217-8309, DOI: 10.18421/TEM84-06, November 2019.


Received: 31 July 2019.

Revised:   05 November 2019.
Accepted:  12 November 2019.
Published: 30 November 2019.




The purpose of this research work is to present in a systematic way the use of the integration method comprising the information system and prediction model towards optimizing the accuracy of Quantity Survey (QS) calculation. The main attention is paid to applied value of the considered methodology, to profitable interpretation and clarification of the results obtained. In order to achieve the goals, information system with the prediction model has been developed and integrated, which predicts the volume of concrete and steel materials using comprehensive experiments over a set of prediction shared algorithms. A new approach to prediction is proposed, based on the use of results of an automatic Information system capable to generate featured to improve the accuracy in the prediction problem. It was experimentally shown that in some cases this approach can be quite effective to significantly improve the quality of prediction and classification.


Keywords – Prediction, Information system, QS, Neural networks, Machine learning.



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