Comparative Analysis of Industrial Mishaps Based on Classified Prediction

Authors(3) :-Nahida Parvin, Ayesha Aziz Prova, Mehnaz Tabassum

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 & Na´ve 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.

Authors and Affiliations

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

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

  1. Han, J., Kamber, M., Pei, J. (2012), “Data Mining: Concepts & Techniques”, third edition, Morgan Kaufmann Publishers an imprint of Elsevier, ISBN: 978-0-12-381479-1.
  2. Jantan, H., Hamdan, A. R., Othman, Z. A. (2009). “Classification for Talent Management Using Decision Tree Induction Techniques.” 2nd Conference on Data Mining and Optimization. 27-28 October 2009. Selangor, Malaysia.
  3. Hoque, M. S., Debnath, A. K., Mahmud, S. M. S. ( ). “Road Safety of Garment Industry Workers in Dhaka City”. Unpublished
  4. Claeson, B. (2012). “Deadly Secrets” (Online). Available from: http:// resources/ DeadlySecrets.pdf. (Access on April 1, 2015). 
  5. TUDelft (2015). “Working Conditions in the Bangladeshi Garment Sector: Social Dialogue and Compliance” (Online). Available from: documents/ country studies/ bangladesh/ Working conditions in the Bangladeshi garment sector Socialdialogueandcompliance.pdf. (Access on April 1, 2015).
  6. Shanthi, S. & Ramani, Dr.R.G. (2012), “Gender Specific Classification of Road Accident Patterns through Data Mining Technique”, IEEE International Conference On Advances In Engineering, Science And Management, pp.359-365.
  7. Besha, T. & Hill, S.,”Mining Road Traffic Accident Data to Improve Safety: Role of Road Related Factors on Accident Severity in Ethiopia”, unpublished.
  8. Zhang, L., Chen, Y., Liang, Y. & Li, N., “Application of Data Mining Classification Algorithms in Customer Membership Card Classification Model”, International Conference on Information Management and Industrial Engineering, pp. 211-215, 2008.
  9. Firoz, A. (2011). “Design of Readymade Garments Industry for Fire Safety” (Online). Available from: http:// ahsan%20firoz.pdf?sequence=1. (Access on April 12, 2015) 
  10. Calvert, J. B. (2002). “The Collapse of Buildings” (Online), Available from: ~jcalvert/tech/failure.htm.  (Access on April 2, 2015) 
  11. MFB (2009). Available from: http://  (Access on April 3, 2015) 
  12. Oxford Dictionaries (2015). Available from: http:// (Access on April 3, 2015) 
  13. Vohra, G., Bhushan, B. (2012). “Data Mining Techniques for Analyzing the Investment Behavior of Customers in Life Insurance Sector in India.” Available From: http:// IJRIM&vol=Volume%202,Issue%209,September-2012. (Access on 25 February, 2015)
  14. Ahmed, F., “Improving Social Compliance in Bangladesh's Ready-made Garment, Industry” (Online). Available from: http://www. (Access on April 10, 2015)
  15. Clean Clothes Campaign (2012), “Hazardous workplaces: Making the Bangladesh Garment Industry Safe” (Online). Available from: http:// (Access on April 10, 2015)  
  16. Ahmed, I., Guan, D., Chung, T.C.,”SMS Classification Based on Niave Bayes Classifier and Apriori Algorithm Frequent Itemset”, IJMLC, vol.4, No. 2, pp. 183-187, 2014.
  17. Shah, M.C., and Jivani, A.G., “Comparison of Data Mining Classification Algorithms for Brest Cancer Prediction”, 4th ICCCNT, 2013.
  18. Bala1, S. & Kumar, K., “A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique”, IJCSMC, Vol. 3, Issue. 7, July 2014, pp: 960 – 967.

Publication Details

Published in : Volume 1 | Issue 6 | November-December 2015
Date of Publication : 2015-12-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 220-226
Manuscript Number : IJSRSET151632
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

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.
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