Forecasting of Factors Affecting Thermiston Work Productivity Estimation by Using Artificial Neural Network

Abstract

The research aims to find factors affecting of Thermiston work productivity and the derivation of an equation to predict the rates of Thermiston work productivity by using artificial neural network technology and compared with traditional methods. The Artificial Neural Network with multilayer by back-propagation error technique for modeling the productivity estimation is used, it is founded that the ANN are able to manage to, can predict the productivity for Thermiston work with a good level of, amount of accuracy where the average accuracy (92.5)% and value of (R) equals to (89.5). Also shown that the more influential factor on the Thermiston work productivity by using technology artificial neural networks technique is the level of work, performance and experience, either according to MLR technique the factors level of work, and the security is the most influential factors, and materials are available depending on the questionnaire was the most influential factor on the Thermiston work productivity. From this study was to draw many conclusions and recommendations for future studies.

Keywords: Factors affecting, Thermiston work, productivity, artificial neural network