Qualitative Analysis of Loss of Control Aircraft Accidents Using Text Mining Techniques

Anderson, Carolina; Smith, MaryJo O.

doi:10.15394/ijaa.2017.1095

Cited by 3 publications

(1 citation statement)

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“…The method considerably inhibits the occurrence of failures in the summarizing part since it is successful for learning if trained in the right way [ 32 ], it interactively evaluates the homogeneous data found and then, builds the topics on top of that if a number of topics desired are not given. An article [ 33 ] highlights the importance of clustering the words for aircraft accidents and implies that text mining approaches such as topic modeling can play a crucial role in understanding complex systems where both qualitative and quantitative data should be evaluated.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep learning-based user experience evaluation in distance learning

et al. 2023

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The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals’ financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users’ emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.

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Section: Background and Related Workmentioning

confidence: 99%

Deep learning-based user experience evaluation in distance learning

et al. 2023

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Analysis of Aviation Safety Management Issues using Text Mining

Kwon,

Lee

2023

J. Korean Soc. Aviat. Aeronaut.

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In this study, a total of 2,584 domestic research papers with the keywords "Aviation Safety" and "Aviation Accidents" were subjected to Text Mining analysis. Various text mining techniques, including keyword frequency analysis, word correlation analysis, network analysis, and topic modeling, were applied to examine the research trends in the field of aviation safety. The results revealed a significant increase in research using the keyword "Aviation Safety" since 2015, with over 300 papers published annually. Through keyword frequency analysis, it was observed that "Aircraft" was the most frequently mentioned term, followed by "Drones" and "Unmanned Aircraft." Phi coefficients were calculated for words closely related to "Aircraft," "Aviation," "Drones," and "Safety." Furthermore, topic modeling was employed to identify 12 distinct topics in the field of aviation safety and aviation accidents, allowing for an in-depth exploration of research trends.

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An Analysis of Fixed-Wing Stall-Type Accidents in the United States

Fala

2022

Aerospace

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Spin training has not been required for students working towards their private or commercial certificates for the past 70 years. Switching to a stall-prevention mindset within training aimed to make spin recovery unnecessary; however, stall-type accidents, consisting of stalls, spins, and spirals, still occur and are highly fatal. Although past studies have analyzed accidents, interviewed pilots at different levels, and made recommendations for changes in the industry, stall-type accidents are no less fatal now, at a fatality ratio of approximately 40–50% yearly. The research discussed in this paper aims to summarize and present accident stall-type statistics in aggregate over the past five decades and motivate future pilot- and training-centered research to address the high presence of stall-type accidents in aviation. Specifically, this article uses NTSB accident reports to answer the research question of whether there have been changes in the prevalence of spins among both fatal and non-datal fixed-wing accidents in the United States over the past sixty years. The methodology breaks down the accident analysis in three groups, based on the time period in which they occurred, due to differences in the reporting methods used. This paper finds that the prevalence and fatality ratio of stall-type accidents has remained high over the past six decades and that stall-type accidents are more than twice as fatal as an average accident. To remedy the high accident count, we recommend experimental ground and simulator-based training to improve pilot knowledge, skill, and performance.

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Qualitative Analysis of Loss of Control Aircraft Accidents Using Text Mining Techniques

Cited by 3 publications

References 1 publication

Deep learning-based user experience evaluation in distance learning

Deep learning-based user experience evaluation in distance learning

Analysis of Aviation Safety Management Issues using Text Mining

An Analysis of Fixed-Wing Stall-Type Accidents in the United States

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