Published in Neural Computing and Applications, 2022
We proposed a deep learning-based prediction model to predict the incident outcomes using the hidden information underlying the data. Read more
Published in Pattern Recognition and Artificial Intelligence (MedPRAI), 2021
We proposed a new methodological approach to extract useful inherent patterns or rules for accident causation using association rule mining (ARM) of both incident narratives (unstructured texts) and categorical data. Read more
Published in Safety Science (Elsevier), 2019
The aim of the study is to develop a novel methodology for prediction of Slip-trip-fall (STF) occurrences using optimized decision tree (DT) classifiers, namely C5.0, classification and regression tree (CART) and random forest (RF). Read more
Published in Computers & Operations Research (Elsevier), 2019
Optimized machine learning algorithms (SVM, ANN) have been applied to predict the accident outcomes such as injury, near miss, and property damage using occupational accident data. Rules are then extracted by incorporating decision tree C5.0 algorithm with PSO-based SVM model. Read more
Published in IEEE Annual India Conference (INDICON), 2017
Parameter optimization of the SVM is performed using grid search (GS), genetic algorithm (GA), and BAT algorithm to predict the occupational incidents. Read more
Published in International Conference on Computational Techniques in Information and Communication Technologies (IEEE), 2016
In this study, a unique method is proposed by developing a text mining based prediction model using fault tree analysis (FTA), and Bayesian Network (BN). Read more