DocumentCode :
1945721
Title :
Application of Artificial Neural Networks for Prediction of Human Work Efficiency in Noisy Environment
Author :
Zaheeruddin ; Garima
Author_Institution :
Dept. of Electr. Eng., Jamia Millia Islamia, New Delhi
Volume :
2
fYear :
2005
fDate :
28-30 Nov. 2005
Firstpage :
842
Lastpage :
846
Abstract :
Conventional computers have been successful for solving many real world problems but the algorithmic requirement limits their usefulness in applications where no exact mathematical relationship between input-output variables can be established. One such problem is the effects of noise pollution on human work efficiency. From the literature survey, it is observed that the human work efficiency depends to a large extent on noise level, type of task, and exposure time. The cause-effect relationships of these parameters are complex and highly non-linear in nature. It is difficult to develop a mathematical model in such situations. Artificial neural network are model-free estimators that do not require articulating a mathematical relationship. They "learn from experience" with numerical data. Hence, an attempt is made in this paper to develop a model for predicting the effects of noise pollution on human work efficiency using neural networks
Keywords :
human factors; learning (artificial intelligence); neural nets; noise (working environment); noise pollution; artificial neural network; cause-effect relationship; human work efficiency prediction model; mathematical relationship model; model-free estimator; noise pollution; noisy environment; Application software; Artificial neural networks; Computational intelligence; Humans; Intelligent networks; Mathematical model; Noise level; Pollution; Predictive models; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
Type :
conf
DOI :
10.1109/CIMCA.2005.1631573
Filename :
1631573
Link To Document :
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