Descriptive and Predictive Analyses of Data Representing Aviation Accidents

Babič, František; Lukacova, A.; Paralič, Ján

doi:10.1007/978-3-319-10383-9_17

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In addition, Nenonen [27], in his study, also mentioned that useful information may also be obtained if injury narratives or incident reports are analyzed properly. Moreover, there are a few more interesting applications using DT found in the refinery industry [28], the petrochemical industry [29], railway [30,31], road [32], and the aviation industry [33]. In summary, it is often seen that the unstructured texts are very important source of information; however, they remain often under-utilized or sometimes unutilized.…”

Section: Introductionmentioning

confidence: 99%

Classification and pattern extraction of incidents: a deep learning-based approach

Sarkar

Vinay

Djeddi

et al. 2022

Neural Comput & Applic

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Classifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. Besides the explicit information, there exist a large amount of hidden information present in a dataset, which cannot be explored by the traditional machine learning (ML) algorithms. There is a scarcity of studies that reveal the use of deep neural networks (DNNs) in the domain of incident prediction, and its parameter optimization for achieving better prediction power. To address these issues, initially, key terms are extracted from the unstructured texts using LDA-based topic modeling. Then, these key terms are added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the adaptive moment estimation (ADAM)-based DNN (i.e., ADNN) for classification, as ADAM is superior to GD, SGD, and RMSProp. To evaluate the effectiveness of our proposed method, a comparative study has been conducted using some state-of-the-arts on five benchmark datasets. Moreover, a case study of an integrated steel plant in India has been demonstrated for the validation of the proposed model. Experimental results reveal that ADNN produces superior performance than others in terms of accuracy. Therefore, the present study offers a robust methodological guide that enables us to handle the issues of unstructured data and hidden information for developing a predictive model.

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