• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.73-85
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• 1999

This study was conducted to reveal the characteristics of the early growth by locality for Korean white pine planted in Gapyung and Kwangju, Kyunggi-Do and Youngdong, Choongchungbuk-Do. The effects of local climatic conditions as one of environmental factors on the growth were also analyzed. For this, several stand variables such as number of trees survived, mean DBH, mean height, basal area per hectare, and volume per hectare by stand age were measured and summarized for each locality. Based on these statistics, periodic annual increments for 8 years from stand age 10 to 18 were calculated for each of stand variables. A topoclimatological technique, for the estimation of local climatic conditions, which makes use of empirical relationships between the topography and the weather in study areas was applied to produce reasonable estimates of monthly mean, maximum, minimum temperatures, relative humidity, precipitation, and hours of sunshine over remote land area where routine observations are rare. From these monthly estimates, 17 weather variables such as warmth index, coldness index, index of aridity etc. which affect the tree growth, were computed for each locality. The periodic annual increments were then correlated with and regressed on the weather variables to examine effects of local weather conditions on the growth. Gapyung area provided the best conditions for the growth of Korean white pine in the early stage and Kwangju area ranked second. On the other hand, the growth pattern in Youngdong ranked last overall as expected. It is also found that the local growth patterns of Korean white pine in juvenile stage were affected by typical weather conditions. The conditions such as low temperature, high relative humidity, and large amount of precipitation provide favorable environment for the growth of Korean white pine. Especially, the diameter growth, basal area growth, and volume growth are mainly influenced by the amount of precipitation. However, it is proved that the height growth is affected by both the precipitation and temperature.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Pediatric Infection and Vaccine
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• v.18 no.2
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• pp.135-142
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• 2011

Purpose : Hospital associated infection (HAI) caused by multidrug-resistant (MDR) microorganisms has been recognized as an important issue in the world, especially in critically ill patients such as the patients admitted in the intensive care unit. There are fewer papers about MDR-HAI in pediatric patients compared to adult patients. In this study, we investigated the incidence and associated factors of MDR-HAI in children admitted to the intensive care unit (ICU) of a university hospital. Methods : We retrospectively evaluated 135 children who were admitted in ICU for at least 3 days between January 2009 and December 2010. HAI cases were divided into MDR-HAI group and non-MDR-HAI group. Clinical characteristics and various associated factors were compared between those groups. Results : In 39 patients, 45 cases of ICU-related HAI were developed. ICU-related HAI incidence was 47.7 per 1000 patientdays. Thirty-six cases (80.0%) were MDR-HAI. Acinetobacter baumannii was isolated more commonly in MDR-HAI group. And the followings were found more frequently in MDR-HAI group than non-MDR-HAI group: medical condition as an indication for ICU admission, mechanical ventilation, urinary catheterization and previous use of broad-spectrum antibiotics. Among the risk factors, previous use of broad-spectrum antibiotics was the independent risk factor for MDR-HAI. Conclusion : ICU-related HAI incidence was higher than previously reported. Previous use of broad-spectrum antibiotics was the independent risk factor for MDR-HAI. To investigate the characteristics of MDR-HAI in children admitted in ICU, further studies with a larger sample size over a longer period of time are warranted.

• Journal of Preventive Medicine and Public Health
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• v.28 no.4 s.51
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• pp.845-862
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• 1995

We followed prospectively some hospital-delivered mothers to identify characteristics of those not initiated breast-feeding and predictors of breast-feeding discontinuation in monthly telephone interviews. Recruits were composed of 482 mothers who delivered their babies at one university hospital and one OB/GYN clinic in September to November 1991. Breast-feeding discontinuation was defined as switch to 100% formula lasting more than one week regardless of solid foods. Average age of the study subjects was 27.3 years of age(standard deviation 3.2). Multiple logistic regression analysis indicated native place, occupation, method of delivery and method of feeding considered to be better for maternal health were statistically significant(p<0.1) between initiators and non-initiators of breast feeding. In starting cohort(N=242) of those initiated breast-feeding, that median of breast-feeding discontinuation were 5 months and 25th and 75th percentiles were 3 and 9 months respectively. In Cox's proportional hazard model, mothers with $10\sim13$ years of education were 2.63 times (95% confidence interval, CI $1.50\sim4.60$) more likely to discontinue than those with less than 9 years of education and those with more than 13 years of education were 3.55 time (95% CI $1.99\sim6.33$). Compared with house wife, mothers with part-time jobs were 1.99 times (95% CI $0.86\sim4.57$) more likely to discontinue and those with employed full-time were 1.55 times (95% CI $0.96\sim2.51$). These results suggest that the predictors of initiation and discontinuation of breast-feeding may be different and different target populations should be selected to promote initiation and to prevent discontinuation of breast-feeding according to the period after birth.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Women's Studies Review
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• v.27 no.1
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• pp.3-36
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• 2010

This paper is based on the certain point that 'the indigenous', which have long been occupied by the Asian patriarchy or the local communities, now calls for the repositioning in the feminist context. 'The indigenous', in one part, generally refer to the matured long-standing traditions and practices of certain regional, or local communities, as a mode of a place specific way of endowing the world with integral meaning. In the narrow definition, it points to the particular form of placed based knowledge for survival, for example, the useful knowledge of a population who have lived experiences of the environment. In the other part, 'the indigenous' could be criticized in the gender perspectives because it has been served as an ideological tool for patriarchy and sexism, which have undermined women's body and subjectivity in the name of the Asian traditional community. That's why the feminists with sensitivity to the discourses of it, may perceive it very differently, still hesitating dealing with the problem. However, even if there are tendencies that the conservatives romanticize local traditions and essentialize 'the indigenous', as it were, it does not exist 'out there'. Then, it could be scrutinized in the contemporary context which, especially, needs to seek the possibility towards the alternatively post - develope mental knowledge system. In the face of global economic crisis which might be resulted from the instrumentalized or fragmented knowledge production system, it's holistic conceptions that human, society, and nature should not be isolated from each other. is able to give an insightful thinking. It will work in the restraint condition that we reconceptualize the indigenous knowledge not as an unchanging artefact of a timeless culture, but as a dynamic, living and culturally meaningful system towards the ecofeminstic indigenous knowledge. And then, indigenous renaissance phenomena which empower non-western culture and knowledge system and generate increased consciousness of cultural membership. Thus, this paper argues that the indigenous knowledges which have been underestimated in the western-centered knowledge-power relations, could be reconstructed as a potential resources of ecological civility transnationally which reconnect individuals and societies with nature.

• Korea journal of population studies
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• v.19 no.2
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• pp.77-105
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• 1996

The rural problems which we are facing start from the extremely small sized population and the skewed population structure by age and sex. Thus we analyzed the change of the rural population. And we analyzed the recent return migration to the rural areas by comparing the recent in-migrants with out-migrants to rural areas. And by analyzing the rural village survey data which was to show the current characteristics of rural population, we found out the effects of the in-migrants to the rural areas and predicted the futures of rural villages by characteristics. The changes of rural population composition by age was very clear. As the out-migrants towards cities carried on, the population composition of young children aged 0~4 years was low and the aged became thick. The proportion of the population aged 0~4 years was 45.1% of the total population in 1970 and dropped down to 20.4% in 1995, which is predicted to become under 20% from now on. In the same period(1970~1995), the population aged 65 years and over rose from 4.2% to 11.9%. In 1960, before industrialization, the proportion of the population aged 0~4 years in rural areas was higher than that of cities. As the rural young population continuously moves to cities it became lower than that in urban areas from 1975 and the gap grew till 1990. But the proportion of rural population aged 0~4 years in 1995 became 6.2% and the gap reduced. We can say this is the change of the characteristics of in-migrants and out-migrants in the rural areas. Also considering the composition of the population by age group moving from urban to rural area in the late 1980s, 51.8% of the total migrants concentrates upon age group of 20~34 years and these people's educational level was higher than that of out-migrants to urban areas. This fact predicted the changes of the rural population, and the results will turn out as a change in the rural society. However, after comparing the population structure between the pure rural village of Boeun-gun and suburban village of Paju-gun which was agriculture centered village but recently changed rapidly, the recent change of the rural population structure which the in-migrants to rural areas becomes younger is just a phenomenon in the suburban rural areas, not the change of the total rural areas in general. From the characteristics of the population structure of rural village from the field survey on these villages, we can see that in the pure rural villages without any effects from cities the regidents are highly aged, while industrialization and urbanization are making a progress in suburban villages. Therefore, the recent partial change of the rural population structure and the change of characteristics of the in-migrants toward rural areas is effecting and being effected by the population change of areas like suburban rural villages. Although there are return migrants to rural areas to change their jobs into agriculture, this is too minor to appear as a statistic effect.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.