Improved prediction of mental workload versus HSE and ergonomics factors by an adaptive intelligent algorithm

Azadeh, A.; Rouzbahman, Mahsa; Saberi, Morteza; Valianpour, F.; Keramati, Abbas

doi:10.1016/j.ssci.2013.03.004

Cited by 30 publications

(16 citation statements)

References 52 publications

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“…As indicated on the figure, a number of workers were identified as outliers as some of the plotted points fell far away from the straight line at the lower left corner and the top. This requires that the management should take proper corrective measures to these workers and this confirmed the earlier studies reported by [6] and [11]. Azadeh et al, [5], reported that several reasons could be attributed to existence of outliers.…”

Section: Identification Of Outlierssupporting

confidence: 83%

“…The answer was selected as yes, no or I don't know. The data in were converted to a discrete range of 1 and 2 (instead of 0, 0.5 and 1 for no, I don't know and yes respectively) to eliminate zero from calculations [11]. For each category (health, safety, environment, ergonomic), the average scores were used as the final scores in the algorithm.…”

Section: Determination Of Inputs and Outputmentioning

confidence: 99%

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Artificial Neural Network Modeling of Job Satisfaction: A Case Study of ICT, Federal University of Agriculture, Makurdi

Ikpambese¹,

Ipilakyaa²,

Achirgbenda³

2017

IJAIS

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Artificial neural network assessment and modeling of job satisfaction of the Information and Communications Technology (ICT) Directorate workers of Federal University of Agriculture, Makurdi was investigated in this study. Modified Nordic Musculoskeletal Disorder (NMDQ) questionnaires which incorporated health, safety and environment factors were used. The questionnaire consisted of a series of objective questions with 'yes', 'no' and 'I don't know' responses and some were multiple choice questions. Parameters such as health, safety, environment and ergonomic factors were obtained from questionnaires for the modelling of workers efficiency and job satisfaction. The efficiency of workers was determined and normal probability curve for the 40 workers was plotted to identify the outliers. The artificial neural network (ANN) modeling method was employed to predict job satisfaction using health, safety, environment and ergonomic factors as input parameters while job satisfaction was the output. Series of network architectures were considered using different training algorithms. The scale conjugate gradient SCG 4 [3-3] 2 1 was adopted as the suitable network architecture for predicting job satisfaction. Result indicated that the predicted values of job satisfaction were in the range of 1.42 -2.00 as compared with the actual values of 1.50 -2.00 obtained from the questionnaires. Statistical indicators of normal error (E), used for validation of the model gave minimal errors and varied in the range of -0.48 -0.08. The plot of the normal probability curve also indicated the presence of outliers or inefficient workers. Whereas most of the workers were satisfied with the existing health, safety, environment (HSE) and ergonomics (E) programs at the work place, some (outliers) were not. The presence of outliers calls for improvement of ergonomic conditions at the ICT directorate.

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Section: Identification Of Outlierssupporting

confidence: 83%

Section: Determination Of Inputs and Outputmentioning

confidence: 99%

Artificial Neural Network Modeling of Job Satisfaction: A Case Study of ICT, Federal University of Agriculture, Makurdi

Ikpambese¹,

Ipilakyaa²,

Achirgbenda³

2017

IJAIS

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“…Step 6.Efficiency estimation: Use the determined best ANN structure to model relation between inputs (IRE) and outputs (lean principles).In order to calculate efficiency scores, we have used the presented steps by Azadeh, Rouzbahman, Saberi, Valianpour, and Keramati (2013):…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Performance optimization of integrated resilience engineering and lean production principles

Azadeh

Yazdanparast

Zadeh

et al. 2017

Expert Systems with Applications

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This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that "pull system" and "fault tolerant" among lean and IRE factors,

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“…After real-time estimation, iterative update g h (m) and covariance of prediction error, the optimal value g h |(m) is obtained. Let t h (m) indicate the real running time of bus m at route link h; let t h (m) * indicate the predicted running time of bus m at route link h, which is adjusted by the adaptive algorithm [34]. The adaptive algorithm is given with the following equations: …”

Section: Adaptive Algorithmmentioning

confidence: 99%

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

Zhong¹,

et al. 2015

PROMET

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Improved prediction of mental workload versus HSE and ergonomics factors by an adaptive intelligent algorithm

Cited by 30 publications

References 52 publications

Artificial Neural Network Modeling of Job Satisfaction: A Case Study of ICT, Federal University of Agriculture, Makurdi

Artificial Neural Network Modeling of Job Satisfaction: A Case Study of ICT, Federal University of Agriculture, Makurdi

Performance optimization of integrated resilience engineering and lean production principles

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

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