Adaptive hybrid ANFIS-PSO and ANFIS-GA approach for occupational risk prediction.
This study attempted to optimize the adaptive neuro-fuzzy inference system (ANFIS) using particle swarm optimization (PSO) and a genetic algorithm (GA) for calculating occupational risk. Numerous studies have shown that the ANFIS is a good approach for predicting engineering problems. However, it is...
| Publicado en: | International Journal of Occupational Safety & Ergonomics Vol. 31; no. 2; pp. 384 - 399 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185658599&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185658599 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10803548 39QK jtl: International Journal of Occupational Safety & Ergonomics issn: 10803548 maglogo: N pubinfo: dt: Jun2025 vid: 31 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 185658599 182339241 185658599 185658599 10.1080/10803548.2024.2444807 185658599 ppf: 384 ppct: 15 formats: tig: atl: Adaptive hybrid ANFIS-PSO and ANFIS-GA approach for occupational risk prediction. aug: au: Achouri, Mourad Zennir, Youcef Tolba, Cherif affil: LRPCSI Laboratory Skikda, University 20 August 1955 Skikda, Algeria sug: subj: Accidents, Occupational Risk Factors Risk Assessment Neural Networks (Computer) Prediction Algorithms Particle Swarm Optimization Genetic Algorithms Machine Learning Algorithms Logistic Regression Support Vector Machine ab: This study attempted to optimize the adaptive neuro-fuzzy inference system (ANFIS) using particle swarm optimization (PSO) and a genetic algorithm (GA) for calculating occupational risk. Numerous studies have shown that the ANFIS is a good approach for predicting engineering problems. However, it is not well investigated in the area of risk assessment. The proposed techniques were evaluated using various statistical indices, i.e., mean absolute error (MAE) and root mean square error (rmse), to characterize their performance. To test the prediction performance of the proposed technique, a comparison with three well-known machine learning approaches, i.e., artificial neural network (ANN), logistic regression (LR) and support vector machine (SVM), was conducted. The obtained results indicate that ANFIS-PSO achieved better prediction performance for both the training and testing phases. Furthermore, the comparative analysis showed that the proposed model is competitive and suitable for occupational risk prediction. pubtype: Academic Journal doctype: algorithm equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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