Articles written in Sadhana
Volume 44 Issue 3 March 2019 Article ID 0055
In healthcare firms, environmental health and safety (EHS) remains as a vital factor as healthcare products pose very intricate problems related to environment safety. The different similar and dissimilar risk factors that prevail in the system have complicated known and unknown causal relationships that are difficult tounderstand and interpret. Hence, improving the EHS remains as a challenge in healthcare industry. A research study is carried out utilizing the data (in conjunction with expert’s opinion) and conditions of a healthcare firm in India to categorize and obtain the prominent risk factors based on identifying the most adverse causal relationship among them. A Fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy DEMATEL)-based approach is designed and employed to assess and rank different EHS risk factors. The trapezoidal fuzzy membership function of the model facilitates better learning of interrelationships in spite of the prevailing vagueness in the causal relationships between the risk factors. The outcomes (the decisive risk factors) out of the experimentation using the proposed methodology strongly coincide with the actual causes of the EHS factorsduring the last one decade. As the proposed approach is found to be very effective in fixing the causal relationships and ranking among the risk factors, this may be successfully employed in similar healthcare firms/ industries for finding out their respective decisive risk factors.
Volume 44 Issue 6 June 2019 Article ID 0151
Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM)technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearestneighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine theproposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or un-safe roads, respectively.