Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM)

Çakıt, Erman; Olak, Andrzej; Karwowski, Waldemar; Marek, Tadeusz; Hejduk, Irena; Taı̈ar, Redha

doi:10.1016/j.ergon.2020.102925

Cited by 34 publications

(16 citation statements)

References 55 publications

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“…The choice for reflective indicators is justified by the fact that the selected indicators are generated by the latent variable, and that changes in the latent variables will echo in the indicators [96,97]. In recent literature, researchers have used PLS-SEM with reflective models [98][99][100].…”

Section: A Structural Equation Modeling (Sem)mentioning

confidence: 99%

Analysis of Operational Communication Through Structural Equation Modeling

et al. 2020

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In this study a model was developed for analyzing communication on the shop floor using a Structural Equation Modeling with Partial Least Squares estimation (Partial Least Square, PLS). The model included as an exogenous variable (independent) Knowledge Management (KM), and four endogenous variables (dependent): Written Communication (WC), Oral Communication (OC), Man interaction with Information and Communication Technologies (M-ICT), and Operational Communication (OpC). The data for the exploratory study were collected in a land-based oil production region in the Northeast of Brazil. The data analysis was performed using the SmartPLS ® software. The structural model supported six of the nine hypotheses proposed, presenting statistical significance and predictive relevance. The evaluation of Pearson's determination coefficients (R 2) demonstrated a satisfactory degree of adjustment and adherence in explaining the variance of endogenous variables. The effect size (f 2) of the variance explained by the exogenous variable in the four endogenous variables was classified as "very strong", as shown: in WC f 2 = 0.802; OC f 2 = 0.825; M-ICT f 2 = 2,534; and OpC f 2 = 2.003. The model presented predictive validity, KM showed effect (q 2) classified as "very high" in the M-ICT and OpC. The effect (q 2) of KM on WC and OC presented predictive relevance between medium and high, with q 2 = 0.337 and q 2 = 0.267, respectively. The causal relationships between the observed variables and the percentage of participation of each indicator in its construct are contributions to the management of information about production.

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Section: A Structural Equation Modeling (Sem)mentioning

confidence: 99%

Analysis of Operational Communication Through Structural Equation Modeling

et al. 2020

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“…Another study in Japan used the integration of structural equation modeling (SEM) as well as the ANFIS algorithm for evaluating the safety paradigm in the petroleum-based sector 30 . Moreover, according to an investigation in southeastern Poland, the researchers applied Partial Least Squares Structural Equation estimation toolbox and ANFIS for modeling components of their survey relating to employees’ safety at work 31 .…”

Section: Introductionmentioning

confidence: 99%

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system

Pourtousi

Khalijian

Ghanizadeh

et al. 2021

Sci Rep

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We employed a new approach in the field of social sciences or psychological aspects of teaching besides using a very common software package that is Statistical Package for the Social Sciences (SPSS). Artificial intelligence (AI) is a new domain that the methods of its data analysis could provide the researchers with new insights for their research studies and more innovative ways to analyze their data or verify the data with this method. Also, a very significant element in teaching is teacher motivation that is the trigger that pushes the teachers forward, depending on some internal and external factors. In the current study, seven research questions were designed to explore different aspects of teacher motivation, and they were analyzed via SPSS. The current study also compared the results by using an adaptive neuro-fuzzy inference system (ANFIS). Due to the similarity of ANFIS to humans' brain intelligence, the results of the current study could be similar to humans regarding what happens in reality. To do so, the researchers used the validated teacher motivation scale (TMS) and asked participants to fill the questionnaire, and analyzed the results. When the inputs were added to the ANFIS system, the model indicated a high accuracy and prediction capability. The findings also illustrated the importance of the tuning model parameters for the ANFIS method to build up the AI model with a high repeatability level. The differences between the results and conclusions are discussed in detail in the article.

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“…21,22 Neuro-fuzzy and Group Method of Data Handling (GMDH) modeling have been applied in many literature works referred to several fields like engineering geology, [23][24][25][26] mechanical engineering, [27][28][29] electrical engineering, 30 science 31 and industrial ergonomics. 32 The results of researches reported that soft computing methods in predicting are accurate and reliable. [23][24][25][26][27][29][30][31][32][33] Thus, according to the importance of the bond strength of the GFRP bars, this research aimed to predict it based on the neuro-fuzzy inference system, artificial neural network and GMDH which operate based on the results of the experimental data collected from various descripts and is expressed based on the terms of bar condition, concrete, and confinement from transverse reinforcements.…”

Section: Introductionmentioning

confidence: 99%

“…32 The results of researches reported that soft computing methods in predicting are accurate and reliable. [23][24][25][26][27][29][30][31][32][33] Thus, according to the importance of the bond strength of the GFRP bars, this research aimed to predict it based on the neuro-fuzzy inference system, artificial neural network and GMDH which operate based on the results of the experimental data collected from various descripts and is expressed based on the terms of bar condition, concrete, and confinement from transverse reinforcements.…”

Section: Introductionmentioning

confidence: 99%

“…In 2020, Zhou et al 21 built an Artificial Neural Network (ANN) model that assessed the effects of various variables on the bond strength based on a large database from an extensive survey of existing single‐lap shear bond tests on FRP‐concrete specimens 21,22 . Neuro‐fuzzy and Group Method of Data Handling (GMDH) modeling have been applied in many literature works referred to several fields like engineering geology, 23‐26 mechanical engineering, 27‐29 electrical engineering, 30 science 31 and industrial ergonomics 32 . The results of researches reported that soft computing methods in predicting are accurate and reliable 23‐27,29‐33 …”

Section: Introductionmentioning

confidence: 99%

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Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

Alizadeh

Naderpour

Mirrashid

2020

Engineering Reports

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The bond strength among the concrete and reinforced bars is one of the most important factors in designing the reinforced concrete structures. One of the serious problems encountered by utilization of steel-reinforcement bars is corrosion when exposed to different environments. Glass fiber-reinforced polymers (GFRPs) are known as a solution to prevent the destruction of the civil infrastructure. The present study attempts to predict the bond strength between the GFRP bars and concrete based on the neuro-fuzzy inference system and artificial neural networks using 159 beam specimens including notched, splice, hinged, and inverted hinged. The neuro-fuzzy inference system consists of five input series. The output, which is the bond strength, is compared with those presented in the various code relations such as ACI 440.1R-15 and CSA S806-12. It was concluded that the outputs are more compatible with the experimental results in comparison with the amounts obtained from the codes equations.

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Assessing safety at work using an adaptive neuro-fuzzy inference system (ANFIS) approach aided by partial least squares structural equation modeling (PLS-SEM)

Cited by 34 publications

References 55 publications

Analysis of Operational Communication Through Structural Equation Modeling

Analysis of Operational Communication Through Structural Equation Modeling

Ability of neural network cells in learning teacher motivation scale and prediction of motivation with fuzzy logic system

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

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