• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.375-385
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• 2015

This study aims to identify geographical variations and factors that affect smoking rates. The data are collected from the Community Health Survey conducted between 2009 and 2011 by Korea Centers for Disease Control and Prevention and other government organizations. Correlation and multiple regression analysis were used to examine the factors influencing smoking rates. For the purpose of investigating regional variations, we employed a decision tree model. The study has found that the significant factors associated with geographical variations in the smoking rates were the rate of hazardous drinking, the completion rate of hypertension education, the experience rate of anti-smoking campaigns, stress awareness rate, hypertension prevalence, health insurance cost, diabetes prevalence, obesity rate, and strength training rate. Convergence-based analysis on geographical variations of the smoking rates is highly important when the regionally customized healthcare programs is implemented. In the future, it is necessary to develop effective program and customized approach for the regions of high smoking rates. Our study is expected to be used as meaningful data for the design of effective health care programs and assessments to lead effective non-smoking program.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1337-1348
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• 2017

In this paper, we develop the high-risk drinking predictive model in Korea using the cross-sectional data from Korea Community Health Survey (2014). We perform the logistic regression analysis, the decision tree analysis, and the neural network analysis using the data mining technique. The results of logistic regression analysis showed that men in their forties had a high risk and the risk of office workers and sales workers were high. Especially, current smokers had higher risk of high-risk drinking. Neural network analysis and logistic regression were the most significant in terms of AUROC (area under a receiver operation characteristic curve) among the three models. The high-risk drinking predictive model developed in this study and the selection method of the high-risk intensive drinking group can be the basis for providing more effective health care services such as hazardous drinking prevention education, and improvement of drinking program.

• Korean Journal of Environment and Ecology
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• v.25 no.5
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• pp.736-746
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• 2011

This study aims tried to present develop a management plan for about old and big trees and legally protected trees by investigating through the present the current status and the distribution of the legally protected trees and old and big tree that range in Gwangju, South Korea. The number of legally protected plants that distributed in Gwangju City is 68 individual trees from 9 species. The largest number was found in Gwangsan-gu (18 trees). The total number of the old and big trees that are distributed in Gwangju City was confirmed as 155 individual trees from 10 species. It must to be corrected to a correct plant species name that species name is written in review articles incorrectly. The average vitality rate of the legally protected plants was 1.2 and the status of ground was 1.7 on average. And the vitality rate of the old and big trees was 1.3 and the status of ground was 2.0 on average. Vitality are not significantly different between the legally protected trees and old and big trees. Ground status of old and big trees worse than legally protected trees. Gingko biloba (found in Gwangsan-gu Songdaedong Daechon village) was only legally protected plant that requires urgent management due to low vitality rate (between 3.2 and 4.0). Among old and big trees Salix glandulosa which is found in Gwangsan-gu Sansu-dong Gamdong village was only one that requires urgent care. The most important status for the old and big tree is securing minimal space for growth. Therefore, we conclude that the most urgent measure in the management and projection of old and big trees and legally protected plants is removal of asphalt or cement above the surface of root system.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.137-152
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• 2004

Assesment of 30 nutritional minerals and 8 toxic elements in hair are very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in the body. A test has been developed that serves this purpose exceedingly well. This test is known as tissue mineral analysis(TMA). TMA is very popular method in hair mineral analysis for health care professionals in over 46 countries' medical center. However, there are some problems. First, they do not have database which is suitable for korean to do analyze. Second, as the TMA results from TEI-USA is composed of english documents and graphic files prohibited to open, its usability is very low. Third, some of them has low level database which is related to TMA, so hairs are sent to TEI-USA for analyzing and medical services. it bring about an severe outflow of dollars. Finally, TMA results are based on the database of american health and mineral standards, it is possibly mislead korean mineral standards. The purposes of this research is to develope the first Intelligent Medical Expert System(IMES) of TMA, in Korea, which makes clear the problems mentioned earlier IMES can analyze the tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods. Pilot test of this systems are increased of business efficiency and business satisfaction 86% and 92% respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.1139-1145
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• 2010

This thesis tried applying Ayurvedic hair and scalp care program and analyzing its effect. Ayurvedic treatment strengthens our immunity through natural healing power. I measured with MIC test and Halo test in order to examine the antibacterial efficacy against colonies which reside in head, when Ayurvedic base oils and essential oils were used on hair and scalp. Finally, I reached the results as follows. I examined the antibacterial efficacy of base oils and essential oils against Bacillus subtilis and Propionibacterium acnes, based on the MIC test with 9 kinds of base oil and 18 kinds of essential oil. By way of experiment, base oils did not show the antibacterial efficacy from all colonies, and in case of essential oils, such as Chamomile, Clary sage, Jasmine, Neroli and Rose oil, the antibacterial efficacy against two colonies did not appear by the measure of below 5%. It turned out that Thyme, Geranium, Lavender and Tea tree had an high effect on two colonies. According to the Halo test which experimented on 7 kinds of bacteria with the essential oils like Geranium, Lavender and Thyme and with the base oils like Sesame and Coconut, it went to prove that base oils was inefficient on antibiosis. It was examined that all of essential oils had the effect on antibiosis from 7 kinds of bacteria. Thyme showed the best efficacy of antibiosis in the MIC and the Halo test.