首页|New Findings from Qingdao University of Technology Describe Advances in Machine Learning (Using Text Mining and Bayesian Network To Identify Key Risk Factors fo r Safety Accidents In Metro Construction)
New Findings from Qingdao University of Technology Describe Advances in Machine Learning (Using Text Mining and Bayesian Network To Identify Key Risk Factors fo r Safety Accidents In Metro Construction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting out of Qingdao, People’s Republic of C hina, by NewsRx editors, research stated, “Complex risk factors make metro const ruction safety accidents prone to occur, and there are various types of accident s. Accident reports record detailed information about different types of acciden ts in text form.” Our news journalists obtained a quote from the research from the Qingdao Univers ity of Technology, “However, effectively utilizing such unstructured data presen ts a significant challenge. Text mining ™provides a viable foundation for addre ssing this challenge, but related studies have limitations in risk feature extra ction and lack of in-depth analysis capability. To address the deficiencies of e xisting studies and provide a feasible strategy for identifying key risk factors in the metro construction domain, this paper proposes an integrated model combi ning TM and machine learning-based Bayesian networks. Firstly, the term frequenc y-inverse document frequency (TF-IDF) algorithm in TM was used to separately ext ract the direct and indirect cause factors from the accident reports, with the m issing factors supplemented using the TextRank algorithm. Then, depending on the assumption of whether to consider the conditional independence between factors, an improved naive Bayesian network (NBN) and a tree-augmented naive Bayesian ne twork (TAN) were built based on the extracted factors and the corresponding acci dent types, respectively, for further in-depth analysis. Finally, the training s et was divided to train the two network models, and sensitivity analysis was use d to identify the key risk factors. Using 162 accident reports from China as an application example, the results showed that TAN exhibited a higher average accu racy (79.62 %) in the test set compared with the improved NBN (71.75 %), and the importance of risk factors for different accident types was successfully ranked from multiple perspectives using TAN. Meanwhile, some n ew insights into metro accidents in China were obtained, which can support decis ion-making for accident prevention and control.”
QingdaoPeople’s Republic of ChinaAsi aBayesian NetworksCyborgsEmerging TechnologiesMachine LearningRisk and PreventionQingdao University of Technology
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