An adaptive neural network algorithm for assessment and improvement of job satisfaction with respect to HSE and ergonomics program: The case of a gas refinery

Azadeh, A.; Rouzbahman, Mahsa; Saberi, Morteza; Fam, Iraj Mohammad

doi:10.1016/j.jlp.2011.01.015

Cited by 41 publications

(27 citation statements)

References 45 publications

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“…The Neural Network Fitting Tool has the possibility to randomly distribute the patterns in all the three categories. The network was trained using "Bayesian-Regularization" back propagation algorithm [3], figure 2. This process is an iterative process, in this case, for training the network a number of 438 iterations was made.…”

Section: The Resultsmentioning

confidence: 99%

Study of the influence of the technological parameters on the weld quality using artificial neural networks

Anghel

Ene

2018

MATEC Web Conf.

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Abstract. This paper presents a study on the weld quality obtained by different values of the input parameters. The weld quality is characterized by two categories of parameters: geometrical parameters and mechanical parameters. They are dependent on the following process parameters: electric arc voltage, electric current intensity, welding speed, the feed wire velocity. Because the dependence between inputs and outputs is a nonlinear one was used an artificial feed forward neural network (ANN). The ANN was trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. This ANN can be used to estimate some parameters from future experiments of the mechanical process.

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Section: The Resultsmentioning

confidence: 99%

Study of the influence of the technological parameters on the weld quality using artificial neural networks

Anghel

Ene

2018

MATEC Web Conf.

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“…Dynamic systems and models of artificial neural networks evaluated aspects related to the level of satisfaction in the service and therefore aided in the evaluation of absences to the work of the respective employees of a company [45]. The work carried out in [46][47][48][49][50] present studies on absenteeism at work treated through intelligent models.…”

Section: Artificial Neural Network and Absenteeismmentioning

confidence: 99%

A hybrid approach of intelligent systems to help predict absenteeism at work in companies

et al. 2019

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In recent years, several surveys have been conducted on absenteeism and how this affects the routine of conducting productive operations in companies. Therefore, having criteria for predicting absenteeism at work can help managers in contingency actions reduce financial losses due to the absence of a worker in their workplace. The objective of this work is to apply the artificial intelligence concepts of a regularized fuzzy neural network, which combines the benefits of artificial neural networks with the fuzzy set theory to obtain more accurate results in predicting corporate absenteeism. The database called absenteeism at work, taken from the UCI Machine Learning Repository, which captured elements of a Brazilian company, was applied in a fuzzy neural network model that allows the calculation of the regressors, defining the estimate of the lack of hours of an employee. The results of the experiments prove that the intelligent model can help in the creation of a specialist system that assists in the prediction of absenteeism.

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“…In addition, there is a close relationship among health, safety, environment and ergonomics factors. The precise consideration of human factors might lead to the reduction of the number of incidents, injuries and unsafe behaviors at workplace and improve health and safety (Azadeh et al, 2011).…”

Section: Hse and Ergonomicsmentioning

confidence: 99%

“…The sixth questionnaire investigates operator's revenue and total job satisfaction. The structured questionnaires of HSE, ergonomics and job satisfaction concepts were developed based on previous studies (Azadeh et al, 2011(Azadeh et al, , 2013. Also the structured questionnaire of RE was developed based on previous studies (Huber et al, 2009;Azadeh et al, 2013).…”

Section: Data Collectionmentioning

confidence: 99%

A unique algorithm for the assessment and improvement of job satisfaction by resilience engineering: Hazardous labs

Azadeh

Bonab

Salehi

et al. 2015

International Journal of Industrial Ergonomics

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An adaptive neural network algorithm for assessment and improvement of job satisfaction with respect to HSE and ergonomics program: The case of a gas refinery

Cited by 41 publications

References 45 publications

Study of the influence of the technological parameters on the weld quality using artificial neural networks

Study of the influence of the technological parameters on the weld quality using artificial neural networks

A hybrid approach of intelligent systems to help predict absenteeism at work in companies

A unique algorithm for the assessment and improvement of job satisfaction by resilience engineering: Hazardous labs

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