• Journal of Preventive Medicine and Public Health
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• v.30 no.2 s.57
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• pp.308-326
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• 1997

Many studies have led to the notion that essential hypertension in adults is the result of a process that starts early in life: investigation of blood pressure(BP) in children and adolescents can therefore contribute to knowledge of the etiology of the condition. A unique longitudinal study on BP in Korea, known as Kangwha Children's Blood Pressure(KCBP) Study was initiated in 1986 to investigate changes in BP in children. This study is a part of the KCBP study. The purposes of this study are to show changes in BP and to determine factors affecting to BP level and change in Korean adolescents during age period 12 to 16 years. A total of 710 students(335 males, 375 females) who were in the first grade at junior high school(12 years old) in 1992 in Kangwha County, Korea have been followed to measure BP and related factors(anthropometric, serologic and dietary factors) annually up to 1996. A total of 562 students(242 males, 320 females) completed all five annual examinations. The main results are as follows: 1. For males, mean systolic and diastolic BP at age 12 and 16 years old were 108.7 mmHg and 118.1 mmHg(systolic), and 69.5 mmHg and 73.4 mmHg(diastolic), respectively. BP level was the highest when students were at 15 years old. For females, mean systolic and diastolic BP at age 12 and 16 years were 114.4 mmHg and 113.5 mmHg(systolic) and 75.2 mmHg and 72.1 mmHg(diastolic), respectively. BP level reached the highest point when they were 13-14 years old. 2. Anthropometric variables(height, weight and body mass index, etc) increased constantly during the study period for males. However, the rate of increase was decreased for females after age 15 years. Serum total cholesterol decreased and triglyceride increased according to age for males, but they did not show any significant trend fer females. Total fat intake increased at age 16 years compared with that at age 14 years. Compositions of carbohydrate, protein and fat among total energy intake were 66.2:12.0:19.4, 64.1:12.1:21.8 at age 14 and 16 years, respectively. 3. Most of anthropometric measures, especially, height, body mass index(BMI) and triceps skinfold thickness showed a significant correlation with BP level in both sexes. When BMI was adjusted, serum total cholesterol showed a significant negative correlation with systolic BP at age 12 years in males, but at age 14 years the direction of correlation changed to positive. In females serum total cholesterol was negatively correlated with diastolic BP at age 15 and 16 years. Triglyceride and creatinine showed positive correlation with systolic and diastolic BP in males, but they did not show any correlation in females. There was no consistent findings between nutrient intake and BP level. However, protein intake correlated positively with diastolic BP level in males. 4. Blood pressure change was positively associated with changes in BMI and serum total cholesterol in both sexes. Change in creatinine was associated with BP change positively in males and negatively in females. Students whose sodium intake was high showed higher systolic and diastolic BP in males, and students whose total fat intake was high maintained lower level of BP in females. The major determinants on BP change was BMI in both sexes.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.173-184
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• 2019

BACKGROUND: This study was carried out to investigate pesticide residues from fifty streams in Korea. Water samples were collected at two times. Thee first sampling was performed from april to may, which was the season for start of pesticide application and the second sampling event was from august to september, which was a period for spraying pesticides multiple times. METHODS AND RESULTS: The 136 pesticide residues were analyzed by LC-MS/MS and GC/ECD. As a result, eleven of the pesticide residues were detected at the first sampling. Twenty eight of the pesticide residues were detected at the second sampling. Seven pesticides were frequently detected from more than 10 water samples. Ecological risk assessment (ERA) was carried out by using residual and toxicological data. Four scenarios were applied for the ERA. Scenario 1 and 2 were performed using LC50 values and mean and maximum concentrations. Scenarios 3 and 4 were conducted by NOEC values and mean and maximum concentrations. CONCLUSION: Frequently detected pesticide residues tended to coincide with the period of preventing pathogen and pest at paddy rice. As a result of ERA, five pesticides (butachlor, carbendazim, carbofuran, chlorantranilprole, and oxadiazon) were assessed to be risks at scenario 4. However, only oxadiazon was assessed to be a risk at scenario 3 for the first sampling. Oxadiazon was not assessed to be a risk at the second sampling. It seems to be temporary phenomenon at the first sampling, because usage of herbicides such as oxadiazon increased from April to march for preventing weeds at paddy fields. However, this study suggested that five pesticides which were assessed to be risks need to be monitored continuously for the residues.