Prediction of Workplace Accidents with Knowledge Discovery Approach Using Weka Software
Bibliographic record
Abstract
Abstract Every year in the world, tens of millions workers become victim of accidents that leads to killing or disabling of them. According to published statistics in the advanced industrial countries, annual from ten workers one becomes victims and as a result of such accidents to destroy five percent of national labor days. Occupational accident primary causes discomfort for the individual worker and employer and secondary causes to loss of capital and shaky economic of society foundations. Therefore predicting occupational accidents in order to plan for adoption safety standards and prevention of occupational accident is great important. So in order to predict the incidence of occupational safety and accident prevention program for drastic job is important. In this regard, the need to factories and industrial complexes to prevent accidents and to protect personnel data mining technique is clearly evident. In this reserch, using Weka software and algorithms for linear regression, loss of workers is predicted. The research findings include knowledge extraction and estimates in connection with incidents that occurred during the years 2011 to 2013 (3 years) in all production areas in Mobarakeh Steel Complex. The analysis of the statistical population consists of 2396 incident has been recorded. The results of this research indicate that the incidents management via knowledge discovery is very useful and can play an important role in the industry. In addition, the quantitative results are also reflected in the tables in the text. Key words: Workplace accidents, data mining Weka software
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".