@ARTICLE{Yadav_Manoj_Work_2021,
 author={Yadav, Manoj and Tandel, Bhaven},
 volume={vol. 46},
 number={No 4},
 journal={Archives of Acoustics},
 pages={677-683},
 howpublished={online},
 year={2021},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={A study was carried to assess the effect of traffic noise pollution on the work efficiency of shopkeepers in Indian urban areas. For this, an extensive literature survey was done on previous research done on similar topics. It was found that personal characteristics, noise levels in an area, working conditions of shopkeepers, type of task they are performing are the most significant factors to study effects on work efficiency. Noise monitoring, as well as a questionnaire survey, was done in Surat city to collect desired data. A total of 17 parameters were considered for assessing work efficiency under the influence of traffic noise. It is recommended that not more than 6 parameters should be considered for ANFIS modeling hence, before opting for the ANFIS modeling, most affecting parameters to work efficiency under the influence of traffic noise, was chosen by Structural Equation Model (SEM). As a result of the SEM model, two ANFIS prediction models were developed to predict the effect on work efficiency under the influence of traffic noise. R squared for model 1, for training data was 0.829 and for testing data, it was 0.727 and R squared for model 2 for training data was 0.828 and for testing data, it was 0.728. These two models can be used satisfactorily for predicting work efficiency under traffic noise environment for open shutter shopkeepers in tier II Indian cities.},
 type={Article},
 title={Work Efficiency Prediction of Persons Working in Traffic Noise Environment Using Adaptive Neuro Fuzzy Inference System (ANFIS) Models},
 URL={http://ochroma.man.poznan.pl/Content/121820/PDF-MASTER/aoa.2021.139644.pdf},
 doi={10.24425/aoa.2021.139644},
 keywords={traffic noise, noise exposure, social questionnaire survey, human work efficiency, ANFIS prediction model},
}