Analysis and Prediction of Industrial Accidents Using Machine Learning

  • Prabavathi Raman, Nandhini Kannan, Subhash Kumar, Kumar Raunak

Abstract

With the different businesses in today’s environment, there is a huge development in the measure of information being created from various sources. With this tremendous measure of information being generated day by day, there is a requirement for the information to be investigated and be managed methodically. There has been an increase in the number of accidents ever since the evolution of such industries.  Even with the diverse industrial safety and accident prevention systems available, they haven’t been efficient in managing a wide range of parameters and be able to effectively predict them by handling a large amount of data. Moreover, with the existing systems, the cost of planning and storing the data is soaring.  In this research, a conceptual system is made that utilizes low cost storage and process data in less time. It additionally utilizes Machine Learning, NLP and Random Forest calculation so as to comprehend and foresee mishaps in Industrial condition. The industrial data is procured from one of the largest industries in Brazil and the world which records the industrial accidents that took place in every nation. The information is investigated and prepared with Machine Learning algorithm so as to comprehend the reasons for such incidents and how the expectation of future accidents can be done. Subsequently, the framework can think about an assortment of parameters and decide future happenings with exactness.

Published
2020-06-06
How to Cite
Prabavathi Raman, Nandhini Kannan, Subhash Kumar, Kumar Raunak. (2020). Analysis and Prediction of Industrial Accidents Using Machine Learning. International Journal of Advanced Science and Technology, 29(04), 4990 - 5000. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/24935