• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.19-27
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• 2023

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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.5 no.1
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• pp.81-91
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• 2010

The article contains comparative analysis of global, regional, local mining review in comparison with the Republic of Kazakhstan. At the article is considered the condition, production and consumption raw materials in the world. For Kazakhstan this branch is one of the most important, which is defining not only the level of the economic development of the country, but also its economical safety, export potential, opportunities for further development. The article represents practical interest for students, masters, doctors, and experts of the branch.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.4 no.2
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• pp.21-58
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• 2009

The Purpose of this study is working out of the scientific-theoretical and practical recommendations directed on perfection of strategic planning of development of the enterprises of mining and gold mining branch. The methodological basis of research is based on the economic theory developed by a domestic and foreign science. At processing, generalisation and a writing of materials of the master's thesis following methods were applied: - supervision, - comparison, - the analysis and synthesis, - methods of an induction and deduction, - statistical groupings, - average and relative sizes, - the system approach. Finally, the theoretical and practical importance of this research consists that results of research will allow generating a basis of statement of effective system of strategic planning of a long-term sustainable development of the gold mining enterprises reducing risk of acceptance of inefficient strategic decisions. I would like to express many thanks to the NGO "Semey- My Home" and "EastGeoResources" LLP for their help and support in providing the data collection and data analysis stages of my research from 2006.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.124-131
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• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• Journal of Soil and Groundwater Environment
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• v.18 no.4
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• pp.40-49
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• 2013

In the beginning of $20^{th}$ century, the coal mining industry had an important role in Japan at which two-thirds of the coal product came from the Kitakyushu-Chikuho District (KCD). As a consequence of mining activities, land-use condition in this district showed notable changes. This paper presented a study of land-use changes in coal mining area by characterizing land-use pattern transition over the last 100 years. In order to carry out the rigorous analysis of land-use, a series of land-use maps over the last 100 years was developed using geographic information systems (GIS). The historic topographic map and another available old data were used to investigate the long-term changes of land-use associated with past mining within the GIS platform. The results showed that the utilization of a series of developed land-use maps successfully indicated the difference of land-use pattern in the KCD before and after the peak of mining activities. The general findings from land-use analysis described that forest and farm lands were lost and turned into abandoned sites in the last 100 years.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.615-643
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• 2019

Now is the time for IS scholars to demonstrate the added value of academic theory through its integration with text mining, clearly outline how to implement this for text mining experts outside of the academic field, and move towards establishing this integration as a standard practice. Therefore, in this study we develop a systematic theory-based text-mining framework (TTMF), and illustrate the use and benefits of TTMF by conducting a text-mining project in an actual business case evaluating and improving hotel service quality using a large volume of actual user-generated reviews. A total of 61,304 sentences extracted from actual customer reviews were successfully allocated to SERVQUAL dimensions, and the pragmatic validity of our model was tested by the OLS regression analysis results between the sentiment scores of each SERVQUAL dimension and customer satisfaction (star rates), and showed significant relationships. As a post-hoc analysis, the results of the co-occurrence analysis to define the root causes of positive and negative service quality perceptions and provide action plans to implement improvements were reported.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.133-143
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• 2020

The purpose of this study is to investigate the economic potential of the regions from the mining sector of North Morowali, Central-Sulawesi, Indonesia, and the formulation of pro-business regional development management that aims to create synergy between the local government and mining sector entrepreneurs. This study uses a descriptive qualitative approach by taking data in the form of primary data from FGD and secondary data observations from statistical bureau data in the North Morowali, Indonesia. The analysis unit uses SWOT analysis to determine the economic potential of the North Morowali and Location Quotient (LQ) to analyze the economic potential of the mining sector. The research period covers one year (2018-2019) in North Morowali, Indonesia. All the mining products have considerable potential as a financing unit in North Morowali, while mining potential has not been maximally exploited. The absence of regulations, facilities such as road access, and optimal land and sea transportation are the causes of the difficulty of optimization and access to explore mining products comprehensively. As a new province at Central Sulawesi, more efforts and the role of government are needed to focus attention to North Morowali as an area with great potential in the mining sector.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.3
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• pp.55-62
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• 2019

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients' concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

• Genomics & Informatics
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• v.2 no.2
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• pp.99-106
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• 2004

In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.