• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6729-6734
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• 2015

Background: To analyze the mortality distribution of nasopharyngeal carcinoma in China from 1991 to 2013, to predict the mortality in the ensuing five years, and to provide evidence for prevention and treatment of nasopharyngeal carcinoma. Materials and Methods: Mortality data for Nasopharyngeal Carcinoma in China from 1991 to 2013 were used to describe its epidemiological characteristics, such as the change of the standardized mortality rate, sex and age differences, urban-rural differences. Trend-surface analysis was used to study the geographical distribution of the mortality. Curve estimation, time series, gray modeling, and joinpoint regression were used to predict the mortality for the ensuing five years in the future. Results: In China, the standardized mortality rate of Nasopharyngeal Carcinoma increased with time from 1996, reaching the peak values of $1.45/10^5$ at the year of 2002, and decreased gradually afterwards. With males being 1.51 times higher than females, and the city had a higher rate than the rural during the past two decades. The mortality rate increased from age 40. Geographical analysis showed the mortality rate increased from middle to southern China. Conclusions: The standardized mortality rate of Nasopharyngeal Carcinoma is falling. The regional disease control for Nasopharyngeal Carcinoma should be focused on Guangdong province of China, and the key targets for prevention and treatment are rural men, especially after the age of 40. The mortality of Nasopharyngeal Carcinoma will decrease in the next five years.

• Journal of Preventive Medicine and Public Health
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• v.47 no.3
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• pp.158-168
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• 2014

Objectives: To identify and evaluate the trend of meal-skipping rates among Korean adolescents with their contributing causes and the influence of household income level on meal skipping. Methods: Using 2008, 2010, and 2012 data from the Korea Youth Risk Behavior Web-based Survey of 222 662 students, a cross-sectional study with subgroup analysis was performed. We calculated odds ratios for skipping each meal 5 or more times in a week by household socio-economic status using a multiple logistic regression model. The secular change in the meal-skipping rates by the students' family affluence scale was analyzed by comparing the meal-skipping students within each subgroup and odds ratios for the same event over time. Results: Through 2008 to 2012, most of the meal-skipping rates generally showed a continuous increase or were almost unchanged in both sexes, except for breakfast skipping in several subgroups. Students in low-income households not living with both parents had the highest meal-skipping rates and odds ratios for frequent meal skipping. In a time-series subgroup analysis, the overall odds ratios for the same event increased during 2008 to 2012, with a slight reduction in the gap between low and higher income levels with regard to meal skipping during 2010 to 2012. Conclusions: Household socio-economic status and several other factors had a significant influence on Korean adolescent meal-skipping rates. Although the gap in eating behavior associated with household socio-economic differences is currently decreasing, further study and appropriate interventions are needed.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Health Policy and Management
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• v.4 no.2
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• pp.77-110
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• 1994

This study is an empiriacl analysis of effects of government intervention on the health care delivery system in Korea. The purposes of this study are to find out the effects of government intervention on the per capita national health expenditure(per capita NHE), crude mortality rate(CMR), and institutional efficiency. Here, the institutional efficiency is defined as a formula shown below: log$\frac{100-curde mortality rate }{per capita NHE}$$\times$100. The formula indicates that the instiutional efficiency increases if the CMR and/or per capita NHE goes down. In the meantime the government intervention is measured by six independent variables: I) the degree of social developments, ii) the numberr of physicians per 100, 000 population, iii) the proportion of specialists among the total physicians, iv) the proportion of public expenditure among the NHE, v) the proportion of public beds to the total number of beds, vi) the proportion of physicians working at the public sector to the total number of physicians. In the above six independent variables iv), v) and vi) are the ones that reflect the degree of government intervention. In actual calculation, the two independent variables v) and vi) are integrated into a new variable based on one to one correspondence. The materials used are the time-series data from 1970 through 1990 in Korea. A path analysis and the time-series regression analysis were adopted to estimate and examine the causal relationship between variables involved. And decomposition of the effect of causal relationship is made to find net effect, direct and indirect effect. The major findings are as follows; 1. The effect of public expenditure, number of physicians per 100, 000 population, the proportion of specialists among the total physicians and social development shows a positive relationship with per capita NHE. Only if the government intervention would be counted, the effects of the number of physicians and the proportion of specialists succeed in containing per capita NHE. 2. In additionn to the above four variables, one additional variable, per capita NHE, was also responsible for the reduction of CMR. The factor of social development found to be the most potent predictor of the CMR reduction. However, the CMR reduction due to government intervention was negligible. 3. Meanwhile, the above four variables were found to was have negative effects on the institutional efficiency. The reverse is true when the government intervention is counted. For example, the number of physicians and the proportion of specialists have played a positive role in raising institutional efficiency via goverment intervention. This comes from the factual effect that the increment of institutional efficiency via the reduction of per capita NHE is bigger than via the reduction of CMR.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.35 no.3
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• pp.167-173
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• 2013

Purpose: The aim of this study is to analyze the series of panoramic radiograph of implant patients using the system to measure peri-implant crestal bone loss according to the elapsed time from fixture installation time to more than three years. Methods: Choose 10 patients having 45 implant fixtures installed, which have series of panoramic radiograph in the period to be analyzed by the system. Then, calculated the crestal bone depth and statistics and selected the implant in concerned by clicking the implant of image shown on the monitor by the implemented pattern recognition system. Then, the system recognized the x, y coordination of the implant and peri-implant alveolar crest, and calculated the distance between the approximated line of implant fixture and alveolar crest. By applying pattern recognition to periodic panoramic radiographs, we attained the results and made a comparison with the results of preceded articles concerning peri-implant marginal bone loss. Analyzing peri-implant crestal bone loss in a regression analysis periodic filmed panoramic radiograph, logarithmic approximation had highest $R^2$ value, and the equation is as shown below. $y=0.245Logx{\pm}0.42$, $R^2=0.53$, unit: month (x), mm (y) Results: Panoramic radiograph is a more wide-scoped view compared with the periapical radiograph in the same resolution. Therefore, there was not enough information in the radiograph in local area. Anterior portion of many radiographs was out of the focal trough and blurred precluding the accurate recognition by the system, and many implants were overlapped with the adjacent structures, in which the alveolar crest was impossible to find. Conclusion: Considering the earlier objective and error, we expect better results from an analysis of periapical radiograph than panoramic radiograph. Implementing additional function, we expect high extensibility of pattern recognition system as a diagnostic tool to evaluate implant-bone integration, calculate length from fixture to inferior alveolar nerve, and from fixture to base of the maxillary sinus.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6860-6868
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• 2015

In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.89-99
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• 2018

The purpose of this study is to analyse the determinants of the change in the welfare expenditure of local governments in 2015. This study analyzed the spatial correlation of welfare expenditure among neighboring local governments and determined the factors affecting the welfare expenditures. According to the results of the study, spatial correlation of welfare expenditure among local governments appears. Determinants, such as socio-economic factors, administrative factors, public financial factors are affecting the amount of the welfare expenditures, but local political factors, and local tax, last year's budgets are not correlated with the amount of local welfare expenditures. In this study, it is significant to found out that the spatial correlation of welfare expenditure among the local governments and to examine the determinants. If possible, it is necessary to analyze the time-series analysis using the multi-year welfare expenditure data, expecially self-welfare expenditures.

• Journal of the Korea Society for Simulation
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• v.21 no.1
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• pp.19-26
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• 2012

In this paper, we study the relationship between FDI(Foreign Direct Investment) and economic growth in Kazakhstan. For this research, we, first, investigate the factors that affect FDI infow to Kazakhstan since its independence and determine the degree of their influence. Second, we study the impact of FDI per capita on GDP per capita. To achieve these goals, an empirical study is conducted with 18 years data from 1992 to 2009 from World Bank Database. Data are analyzed using multiple linear regression, time series analysis and Granger causality test. The results show that the determinant of FDI is GDP and economic freedom index in Kazakhstan. Economic growth is affected by FDI, too. Specially, FDI is positively related to GDP and economic freedom index. FDI per capita's impact on GDP per capita is 30.4 dollars increase in GDP per capita by one dollar increase in FDI per capital inflow. The results provides useful information for policy makers to improve obtaining large amount of investments and facilitate economic growth.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.167-186
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• 2023

Purpose - The purpose of this study is to empirically investigate whether the auditor accreditation system for IPO firms improves the efficiency of the KOSDAQ IPO market. To verify the effectiveness of the auditor designation system, we time series compare four measures of IPO firms (earnings management, long-term stock performance, change in operating performance, and possibility of delisting). Design/methodology/approach - We test the hypothesis through event research method and regression analysis. Specifically, the dependent variables of the regression model are discretionary accruals in the year of IPO, 36-month holding period excess return after IPO, change in operating performance for 3 years after IPO, and dummy variable for delisting. And the explanatory variable is a dummy variable that separates the period before and after the implementation of the auditor designation system. Findings - We find that earnings management and delisting risks decreased more in the period after the implementation of the auditor accreditation system than in the previous period. In addition, we find that long-term stock performance and operating performance after IPO increase further after the implementation of the auditor accreditation system. Research implications or Originality - Overall, the results of this study suggest that the implementation of the auditor accreditation system for IPO firms contributes to improving market efficiency in the KOSDAQ market, where information asymmetry is high. Our study differs from previous studies in that it demonstrates the effectiveness of the auditor designation system using various measures.