A Multi Objective approach for selecting solutions to improve job satisfaction an empirical case analysis

Baradaran, Vahid; Ghadami, Sajjad; Malihi, S. E.

doi:10.1109/ieem.2008.4738211

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Chen (2008) examined relationships between achievement motivation and job characteristics on job satisfaction among IS personnel. Baradaran, Ghadami, and Malihi (2008) used multi objective model to select effective solutions for improving job satisfaction. Five components of job satisfaction were revealed as the objective functions by principal component analysis, and the effective solutions were determined by multi objective analysis.…”

Section: Job Satisfactionmentioning

confidence: 99%

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

Rouzbahman

Saberi

et al. 2011

Journal of Loss Prevention in the Process Industries

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Section: Job Satisfactionmentioning

confidence: 99%

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

Rouzbahman

Saberi

et al. 2011

Journal of Loss Prevention in the Process Industries

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“…The confusion matrix results ( Table 4 ) and ROC curve ( Fig. 6 ) demonstrated that the PST-NN model was highly efficient, in terms of psychosocial risk classification, as compared to other experiments and models ( Larrabee et al, 2003 ; Baradaran, Ghadami & Malihi, 2008 ; Aliabadi, Farhadian & Darvishi, 2015 ; Farhadian, Aliabadi & Darvishi, 2015 ; Yigit & Shourabizadeh, 2017 ; Jebelli, Khalili & Lee, 2019 ). The level of precision and low error percentage of PST-NN approach demonstrated the ease adaptation of the mathematical structure to the input variables, generating a model that can be used to perform preventive interventions in occupational health by way of prediction, based on psychosocial, physiological, and musculoskeletal factors.…”

Section: Discussionmentioning

confidence: 84%

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

Navarro

Castrillón

Osorio

et al. 2021

PeerJ Computer Science

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Background Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs. Methods The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model. Results The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%. Conclusions The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it.

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Material and Structural Optimization for Engine Hood Inner Panel of Car Body Aimed to Lightweight

Liu

2010

2010 WASE International Conference on Information Engineering

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A Multi Objective approach for selecting solutions to improve job satisfaction an empirical case analysis

Cited by 3 publications

References 14 publications

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

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

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers

Material and Structural Optimization for Engine Hood Inner Panel of Car Body Aimed to Lightweight

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