• Journal of Nutrition and Health
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• v.52 no.5
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• pp.449-464
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• 2019

Purpose: North Koreans could be at higher risk for their bone health because of previous periods of severe famine and the continuing low availability of food. This study determined the bone mineral density (BMD) status and its relationship with dietary behaviors and nutrient intake of North Korean refugees (NKR) in South Korea (SK). Methods: This cross-sectional study analyzed 110 female NKR from a NORNS cohort of a non-probability sample of adult NKR in Seoul. BMD examined by DEXA was used to divide participants into the normal group (NG) and the non-normal group (NNG) according to the WHO guideline. A self-administered questionnaire included questions on age, the socioeconomic situation in North Korea (NK) and SK, the food security in NK and SK, and the health behaviors, dietary behaviors, and food frequency questionnaire administered in SK. A one-day 24-hr recall was conducted and the results were analyzed by using CanPro. SPSS was used to analyze whether BMD and related dietary behaviors and nutrient intakes differed according to the groups. Results: NG (62.7%) was significantly younger and had a lower abdominal obesity score than NNG (p < 0.001). While 14.5% of NG reported experiencing menopause, all of NNG reported experiencing menopause. The NG more frequently consumed the dairy group of foods (9.6 times a week) than did the NNG (4.8 times a week) after the statistics were adjusted for age (p < 0.007). The NG consumed significantly more animal protein and animal calcium than did the NNG (p = 0.01, p = 0.009, respectively). Calcium intake was low with 49.3% of NG, and 78.0% of the NNG reported consuming calcium lower than the estimated average requirement. Only calcium showed an index of nutrient quality lower than one in both groups. Conclusion: These results showed that NKR women and possibly all North Korean women are at high risk for bone health and they consumed low levels of bone-related nutrients, and this should be considered for the nutrition policy for NKR and North Korea.

• 한국사회정책
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• v.20 no.3
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• pp.77-115
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• 2013

The purpose of this study is to present empirical evidence for discussion of financing social welfare via estimating optimal tax burden in the main member countries of the OECD by using Hausman-Taylor method considering endogeneity of explanatory variables. Also, the author produced an international tax comparison index reflecting theoretical hypotheses on revenue-expenditure nexus within a model to compare real tax burden by countries and to examine feasibility of tax increase in Korea. As a result of the analysis, the higher the level of tax burden was, the higher the level of welfare expenditure was, indicating the connection between high burden and high welfare from the aspect of scale. The results also indicated that the subject countries recently entered into the state of low tax burden. Meanwhile, Korea had maintained low burden until the late 1990s but the tax burden soared up since the financial crisis related to the IMF. However, due to the impact of foreign economy and the tax reduction policy, it reentered into the low-burden state after 2009. On the other hand, the degree of social welfare expenditure's reducing tax burden has been gradually enhanced since the crisis. In this context, the current optimal tax burden ratio of Korea as of 2010 may be 25.8%~26.5% of GDP based on input of welfare expenditure variables, a percent that Korea was investigated to be a 'high tax burden-low ITC' country whose tax increase of 0.7~1.4%p may be feasible and that the success of tax system reform for tax increase might be higher probability when compare to others. However, measures of increasing social security contributions and consumption tax were analyzed to be improper from the aspect of managing finance when compared to increase in other tax items, considering the relatively higher ITC. Tax increase is not necessarily required though there may be room for tax increase; the optimal tax burden ratio can be understood as the level that may be achieved on average when compared to other nations, not as the "proper" level. Thus, discussion of tax increase should be accompanied with comprehensive understanding of models of economic developmental difference from nations and institutional & historical attributes included in specific tax mix.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Korean Journal of Agricultural Science
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• v.7 no.2
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• pp.87-102
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• 1980

In order to investigate the utilization probability of two years old laying hen for W.L. and R.I.R. breeds, carcass weight and percentage were examined and dried old chicken meat products were manufactured for experiments. The results obtained are as follows. 1. Average living body weight were 1,635.40g for the W.L. breeds and 2,289.29g for the R.I.R. breeds and percentage carcass and lean meat for the W.L. were 58.73% and 43.95%, for the R.I.R. 60.34%, 41.98%, respectively. 2. In constitution percentage of carcass on different parts for W.L. and R.I.R. breeds, head were 4.13% and 3.94%, wing 9.97% and 8.62%, breast 32.54% and 20.94%, back 11.35% and 9.75%, thigh 30.75% and 31.34%, hypordermic fat 11.37% and 17.34%, respectively. 3. In constitution percentage of lean meat on different parts for W.L. and R.I.R. breeds, head were 4.03% and 3.95%, wing 9.47% and 9.79%, breast 39.37% and 38.14%, back 11.24% and 9.40%, thigh 36.16% and 38.74%, respectively. 4. In chemical composition of old chicken meat for W.L. breed, moisture was 68.18%, crude protein 22.80%, crude fat 2.70%, extract 5.15% and crude ash 1.18% and for R.I.R. breed, moisture was 68.04%, crude protein 22.18%, crude fat 3.13%, extract 5.45% and crude ash 1.21%. 5. Weight loss in steaming for W.L. at $121^{\circ}C$ for 30min., 60min., and 90min. were 54.91, 56.43 and 58.42%, respectively, and for R.I.R. were 45.23, 47.68 and 49.68%, respectively. 6. The yield of old chicken meat product per a hen were 253.01g for W.L. and 368.64g for R.I.R., the ratio for fresh meat weight and for carcass weight were 35.47% and 26.34% for W.L. breed and 38.25 and 26.83% for R.I.R. breed. 7. In chemical composition of old chicken meat product for W.L., moisture was 16.69%, crude protein 66.16%, crude fat 12.81%, crude ash 4.35%, and R.I.R., moisture 16.11%, crude protein 65.95%, crude fat 13.78% and crude ash 4.57%. 8. To investigate the physical properties which was main factor affecting the product quality, tensile strength, tear strength and elongation rate were measured. The adhesive force of the product made under pressure of $70kg/cm^2$ was similar to those of chipo which was the control product. 9. When measured the color of each protein product, lightness of the product pressed at $70kg/cm^2$ was better than that at $35kg/cm^2$, and the lightness of breast muscle product at $70kg/cm^2$ and chipo was not significant as 16.7% and 16.4%, respectively. Dominant wavelength of product pressed at $70kg/cm^2$ was very similar to chipo which was yellowish orange. 10. In the results of sensory evaluation test containing taste, color, chewing texture and oder of the meat product, when index of chipo as control product was 100, index of breast meat product was higher than that as 118.4, but miscellaneous product was 99.7 and thigh product was 96.2. 11. Summing up the results written above, the meat product utilizing two years old laying hen was compared favorably with its similar food such as chipo on the point of nutrition and physical properties as high protein food, therefore, it was thought that industrialization must be highly appropriate.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.