Seasonal-Wise Occupational Accident Analysis Using Deep Learning Paradigms

In recent years, occupational accidents causes a huge loss of human life and the development of the economy of the country. Many techniques are evolved for automating the safety precautions for employees in the industrial sectors such as mining, metals, construction, chemical, and electrical sections. However, the automation cannot be accurate as the data analysis is based on real-life data. Since the real-life data are imbalanced and uncertain, it is necessary to identify better tools to overcome these issues. Thus the proposed model utilizes SMOTE (Synthetic Minority Over-sampling Technique) for data balancing, whereas a rough set is used for identifying the significant features that help to maintain data consistency. The consistent data is then applied to the Deep Neural Network (DNN) for the classification process. The performance of the proposed model is checked against the evaluation metrics and compared with the existing deep learning models to exhibit the efficiency of the proposed model. Thus the findings of the proposed model may improve the abilities of safety professionals in the industrial sector to develop safety intervention activities.