• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.365-374
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• 2018

This study investigated the relationship between Vitamin D levels and indices related to cardiovascular disease in Korean adults aged ${\geq}19years$. The data for analysis were obtained from the sixth Korea National Health Nutrition Examination Survey in 2013 and 2014. The results showed that the incidence of Vitamin D deficiency was 73.1% among Korean adults and that indices related to cardiovascular disease showed an increasing trend (55.6% of Korean adults). The relationship between Vitamin D levels and indices related to cardiovascular disease with controlled physical activity status was also studied. The odds ratios (ORs) for age were 1.72 for the 4-62 years age group (95% CI, 1.53-1.93) and 2.05 for the ${\geq}65years$ age group (95% CI, 1.71-2.45). For blood pressure, the OR for pre-hypertension was 1.30 (95% CI, 1.15-1.47) and that for hypertension was 1.31 (95% CI, 1.11-1.54). For body mass index (BMI), the OR was 1.36 (95% CI, 1.11-1.66) and that for waist circumference (WC) was 1.36 (95% CI, 1.11-1.66). For fasting blood sugar (FBS), the OR for impaired fasting glucose (IFG) was 1.37 (95% CI, 1.21-1.55) and that for diabetes mellitus (DM) was 1.31 (95% CI, 1.05-1.65). The OR for total cholesterol (TC) was 1.30 (95% CI, 1.11-1.52) and that for triglycerides (TG) was 1.20 (95% CI, 1.04-1.37) in Korean adults. There was a significant relationship between Vitamin D and indices related to cardiovascular disease in Korean adults with respect to age, blood pressure, FBS, BMI, TC and TG. Confirmation of a causal relationship between Vitamin D and indices related to cardiovascular disease may require further research consisting of more systematic cohort studies.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.4
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• pp.37-52
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• 2022

The purpose of this study is to analyze the demand for cultural ecosystem services in urban forests based on user perception and experience value by using Naver blog posts and LDA topic modeling. Bukhansan National Park was used to analyze and review the feasibility of spatial assessments. Based on the results of topic modeling from blog posts, a review process was conducted considering the relevance of Bukhansan National Park's cultural services and its suitability as a spatial assessment case, and finally, an index for the spatial assessment of urban forest's cultural service was derived. Specifically, 21 topics derived through topic analysis were interpreted, and 13 topics related to cultural ecosystem services were derived based on the MA(Millennium Ecosystem Assessment)'s classification system for ecosystem services. 72.7% of all documents reviewed had data deemed useful for this study. The contents of the topic fell into one of the seven types of cultural services related to "mountainous recreation activities" (23.7%), "indirect use value linked to tourism and convenience facilities" (12.4%), "inspirational activities" (11.2%), "seasonal recreation activities" (6.2%), "natural appreciation and static recreation activities" (3.7%). Next, for the 13 cultural service topics derived from data gathered about Bukhansan National Park, the possibility of spatial assessment of the characteristics of cultural ecosystem services provided by urban forests was reviewed, and a total of 8 cultural service indicators were derived. The MA's cultural service classification system for ecosystem services, which was widely used in previous studies, has limitations in that it does not reflect the actual user demand of urban forests, but it is meaningful in that it categorizes cultural service indicators suitable for domestic circumstances. In addition, the study is significant as it presented a methodology to interpret and derive the demand for cultural services using a large amount of user awareness and experience data.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.