5.Study of optimized SVM for incident prediction of a steel plant in India

Abstract: Occupational accident is a serious issue for every industry. Steel industry is considered to be one of the economic sectors having a high number of accidents. Thus, the main aim of this study is to build a model which could predict the occupational incidents (i.e., injury, near-miss, and property damage) using support vector machine (SVM) by utilizing a database comprising almost 5000 occupational accidents reports from an integrated steel plant corresponding to the span of years 2010 to 2012. Parameter optimization of the SVM is performed using grid search (GS), genetic algorithm (GA), and BAT algorithm to obtain the better accuracy of the classifier. The results of experiments show that grid search-based SVM outperforms other optimized SVM approaches with 88.0% accuracy. Other optimization techniques can also be adapted to search for the better prediction accuracy of the model.

Recommended citation: Sarkar, S., Vinay, S., Pateshwari, V., & Maiti, J. (2016, December). Study of optimized SVM for incident prediction of a steel plant in India. In 2016 IEEE Annual India Conference (INDICON) (pp. 1-6). IEEE.