Comparative Analysis of Industrial Mishaps Based on Classified Prediction

Authors

  • Nahida Parvin  Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
  • Ayesha Aziz Prova  Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
  • Mehnaz Tabassum   Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh

Keywords:

Data Mining, Classification Algorithms, Meta Classifier, Textile & Garments Accident Data

Abstract

Industrial accident analysis is a very challenging task and one of most vital issues in the era of globalization. Discovering the attributes becomes more complex because voluminous factors are associated. We have tried to find the specific attributes and made a cumulative dataset depending on the reliable sources allied to Bangladesh. In the study, we have evaluated a meticulous survey on various classification techniques to achieve casualty for textile & garments accidents. We have presented a comparative analysis of accuracy between base and AdaBoost Meta classifier using base classifiers, such as: OneR, J48, REPTree, SimpleCART & Naïve Bayes. The analysis unfurl that using ensemble method with the base classifiers improve accuracy level between 1.8%-6.36%. Depending on the knowledge explored by classification technique which will have the ability to make automated decision that is quite similar to human decision making for reducing the rate of casualty of industrial mishaps.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2015-12-25

Issue

Volume 1, Issue 6, November-December-2015

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Nahida Parvin, Ayesha Aziz Prova, Mehnaz Tabassum , " Comparative Analysis of Industrial Mishaps Based on Classified Prediction , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 6, pp.220-226, November-December-2015.