• Korean Journal of Occupational Health Nursing
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• v.21 no.3
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• pp.247-257
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• 2012

Purpose: This study is designed to identify major factors that affect morale among staff of long-term care insurance in National Health Insurance Corporation. Methods: In order to collect the data, a survey was conducted by using the structured questionnaire based on 161 staff members of long-term care insurance of 50 long-term care operation centers, which were randomly selected by the table of random numbers in the whole centers of National Health Insurance Corporation from September 1 to 30, 2011. As for the data analysis, SPSS 18.0 was used to conduct the descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, mutiple regression, and path analysis. Results: The average point of morale was 3.37, and had a negative correlation with the job stress, while it had a positive correlation with the social support, professional identity, and self-efficacy. Job stress, social support, and professional identity have a significant effect on morale among the independent variables. These variables have significant effects on morale, and also have a positive effect on self-efficacy. The findings show that self-efficacy mediates the process of morale. Conclusion: In this study, the factor which influences the morale was identified. It turned out that the morale could be improved by reinforcing the professional identity, managing the health status as well as rotating the working place. As such, it expects both the improvement of long-term care insurance services outcome and its quality through the morale management.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Land and Housing Review
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• v.14 no.2
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• pp.1-17
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• 2023

In Korea, the population size of the elderly is rapidly increasing, and housing for them is emerging as an important issue. In particular, Aging in Place (AIP) has steadily been presented as a direction of welfare for the elderly. This study empirically examines the effect of the interactions of the elderly with their children on residential mobility for older Koreans. To do so, this study employed random effect logistic regression models with the dataset of the 2008-2020 Korean Longitudinal Study of Aging. The findings are as follows. First, it was found that the interaction with their children increased the probability of residential mobility for older Koreans in both metropolitan areas and non-metropolitan areas. Second, as age increased, the interaction with their children tended to further promote residential mobility for older Koreans, but such effects varied depending on related variables. Third, it was confirmed that the possibility of further promoting residential mobility for older Koreans increased through the interaction effects of the variables associated with the interaction with their children. This study suggests policy implications for the residential mobility of older Koreans, i.e., whether the interactions with their children improve independent residential environments by enhancing housing stability, in terms of AIP.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

• Convergence Security Journal
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• v.8 no.3
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• pp.83-92
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• 2008

In respect complication of group and period, the sales of retail trade is composed of various factors. This paper studies focus on estimating the determinants of the sales of retail trade. The volume of analysis consist of 7 groups. Analyzing period be formed over a 36 point(2005. 1$\sim$2007. 12). In this paper dependent variable setting up sales of retail trade, explanatory(independent) variables composed of composite stock price index, the number of the consumer's online buying behavior company, the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry), the consumer price index. The result of estimating the determinants of sales of retail trade provides empirical evidences of significance positive relationships between the coincident composite index, the index of trading price of APT, employment rate, an average of the rate of operation(the manufacturing industry). However this study provides empirical evidences of significance negative relationships between the consumer price index. The explanatory variables, that is, composite stock price and the number of the consumer's online buying behavior company, are non-significance variables. Implication of these findings are discussed for content research and practices.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.9
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• pp.825-830
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• 2009

Pharmaceutical compounds enter the water environment through the diverse pathways. Because their concentration in the water environment was frequently detected in the level of ppt to ppb, the monitoring system should be optimized as much as possible for finding appropriate management policies and technical solutions. One Factor At a Time (OFAT) approach approximating the response with a single variable has been preferred for the optimization of LC-MS/MS operational conditions. However, it is common that variables in analytical instruments are interdependent. Therefore, the best condition could be found by using the statistical optimization method changing multiple variables at a time. In this research, response surface analysis (RSA) was applied to the LC-MS/MS analysis of emerging antibiotic compound, sulfamethoxazole, for the best sensitivity. In the screening test, fragmentation energy and collision voltage were selected as independent variables. They were changed simultaneously for the statistical optimization and a polynomial equation was fit to the data set. The correlation coefficient, $R^2$ valuerepresented 0.9947 and the error between the predicted and observed value showed only 3.41% at the random condition, fragmentation energy of 60 and collision voltage of 17 eV. Therefore, it was concluded that the model derived by RSA successfully predict the response. The optimal conditions identified by the model were fragmentation energy of 116.6 and collision voltage of 10.9 eV. This RSA can be extensively utilized for optimizing conditions of solid-phase extraction and liquid chromatography.

• Journal of the Korean Society of School Health
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• v.12 no.1
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• pp.45-56
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• 1999

Adolescence is vulnerable to various Health Risk Behaviors (HRB). These behaviors can affect his remaining life as well as adolescence, thus prevention of HRB is a critical issue in health education. This study is aimed to provide basic information for prevention of HRB. Thus, this study was conducted to analyze the impact of peer group's health risk behaviors on health risk perception (HRP) and that of health risk perception on health risk behaviors based on 832 respondents. The 852 subjects were selected in six middle and high schools in Seoul through random sampling. Data were collected from September, 18-October, 21, 1998, and the 832 data were analyzed after excluding the 20 incomplete and inaccurate data. Questionnaire items and measures are based on an instrument to measure Perceived Health Risk Perception, which Hodge B.C. developed in 1992. Cronbach alpha is used to test the reliability. The reliability of HRP and HRB is 0.9473, 0.8768 in this study, Statistical analysis divided into four phases. First, the impact of socio-demographic characteristics on HRP is analyzed by oneway ANOV A. Male students have lower HRP than female students. As grade goes up, HRP is getting lower. Perceived higher concern of parents and HRP are correlated. And the experience of school health education and HRP are correlated. Second, the impact of peer group's HRB on the HRP is analyzed by linear regression. Peer group's HRB and HRP are negatively correlated, Third, the impact of HRP on HRB is analyzed by linear regression. There is a correlation between high HRP and low HRB. Fourth, Powerful impact factors on HRB are analyzed by stepwise multiple regression. Grade, gender, peer group's HRB, and related HRP is entered as independent variables. Because of correlation between entered variables, three interaction variables between grade, gender, peer group's HRB and related HRP also entered, In general, peer group's HRB is the most accountable factor to HRB. And Interaction variable between HRP and peer group's HRB and HRB are negatively correlated. These results indicate that HRP may reduce the impact of peer group's HRB on HRB. Some recommendations are as follows: First, health educational programs suitable for gender and grade are required. Second, a systematic cooperation between school and home is necessary for effective prevention of HRB. Third, the educational effect for decreasing HRB by increasing HRP is statistically assisted. However, peer group has much stronger impact on HRB than subjective HRP, thus special consideration and management are necessary for peer group which does HRB more frequently.