Neuro-fuzzy prediction model of occupational injuries in mining.

Objectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, mach...

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Publicado en:International Journal of Occupational Safety & Ergonomics Vol. 31; no. 1; pp. 24 - 34
Autores principales: Ivaz, Jelena S., Petrović, Dejan V., Stojadinović, Saša S., Stojković, Pavle Z., Petrović, Sanja J., Zlatanović, Dragan M.
Formato: research tables/charts Journal Article
Publicado: Taylor & Francis Ltd Mar2025
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Mar2025
      vid: 31
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/10803548.2024.2401678
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        atl: Neuro-fuzzy prediction model of occupational injuries in mining.
      aug:
        au:
          Ivaz, Jelena S.
          Petrović, Dejan V.
          Stojadinović, Saša S.
          Stojković, Pavle Z.
          Petrović, Sanja J.
          Zlatanović, Dragan M.
        affil: Technical Faculty in Bor, University of Belgrade, Serbia
      sug:
        subj:
          Prediction Models
          Occupational-Related Injuries Risk Factors
          Risk Assessment
          Mining Serbia
          Neural Networks (Computer)
          Occupational Safety
          Multilayer Perceptrons
          Accidents, Occupational Prevention and Control
          Occupational-Related Injuries Epidemiology
          Human
          Serbia
          Occupational-Related Injuries Prevention and Control
          Personal Protective Equipment
          Communication
          Blue Collar Workers
          Sensitivity and Specificity
          Descriptive Statistics
      ab: Objectives. This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mineral extraction involving people, machinery and limited workplaces. Methods. A universal model for predicting occupational accidents takes into account influential factors such as organizational aspects, personal and collective protective equipment, on-the-job training and leadership factors. The selected networks achieved a prediction accuracy of >90%. Results. The study successfully identifies potential risks and critical worker groups leading to injuries. The sensitivity analysis provides insights for targeted safety measures and improved organizational practices. Conclusion. This data-driven approach makes a valuable contribution to safety in the mining industry. Implementation of the predictive model can reduce injuries and machine damage, and improve worker well-being.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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