• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• The Korean Journal of Air & Space Law and Policy
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• v.27 no.2
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• pp.67-106
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• 2012

Today air traffic at the airport is complicated including a significant increase in the volume of air transport, so aviation accidents are constantly occurring. Therefore, we should newly recognize importance of the Air Traffic Safety, the core values of the Air Traffic. The location of airport that is the basic infrastructure of the air traffic and the security of safety for facilities and equipments are more important than what you can. From this dimension, I analyze the step-by-step safety factors that are taken into account in the airport development projects from the construction or improvement of the airport within the current laws and institutions and give my opinion on the enhancement of safety in the design and construction of airport. The safety of air traffic, as well as airport, depends on location, development, design, construction, inspection and management of the airport including airport facilities because we have to carry out the national responsibility that prevents the risk of large social overhead capital for many and unspecified persons in modern society through legislation regarding intervention of specialists and locational criteria for aviation safety from the planning stage of airport development. In addition, well-defined installation standards of airports and air navigation facilities, the key points of the airport development phase, can ensure the safety of the airport and airport facilities. Of course, the installation standards of airport and air navigation facilities are based on the global standard due to the nature of air traffic. However, to prevent the chaos for the safety standards in design, construction, inspection of them and to ensure the aviation safety, the safety standards must be further subdivided in the course of domestic legislation. The criteria for installation of the Air Navigation facilities is regulated most specifically. However, to ensure the safety of the operation for Air Navigation Facilities, performance system proved suitable for the Safety of Air Navigation Facilities must change over from arbitrary restrictions to mandatory restrictions and be applied for foreign producers as well as domestic producers. Of course, negligence of pilots and defective aircraft maintenance lead to a large portion of the aviation accidents. However, I think that air traffic accidents can be reduced if the airport or airport facility is perfect enough to ensure the safety. Therefore, legal and institutional supplement to prioritize the aviation safety from the stage of airport development may be necessary.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.27 no.4
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• pp.398-410
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• 1982

Summer crops grown in uplands are greatly diversified and show a large variation in difference with year and location in Korea. The principal factor for the variation is weather, in which precipitation and temperature play a leading role and such a weather factors as wind, sun lights also influence production of the summer crops. Since artificial control of weather conditions as a main stress factor for crop production is almost impossible, it must be minimized only by an improvement of cultivation techniques and crop improvement. Precipitation plays a role as one of the most important factor for production of the summer crops and it is considered in two aspects, drought and excess moisture. This country, which belongs to monsoon territory, necessarily encounter one of this stress almost every year, even though the level is different. Therefore, the facilities for both drought and excess moisture are required, but actually it is not easy to complete for them. On this account, crops tolerant to drought, excess moisture and pests should be considered for establishing summer crops. For the districts damaged habitually every season, adequate crops should be cultured and appropriate method of planting, drainage and weed control should be applied diversely. Injuries by temperature is mainly attributed to lower temperature particularly in late fall and early spring, although higher temperature often causes some damages depending upon the kind of crops. Sometimes, lower temperature in summer season playa critical role for yield reduction in the summer crops. However, certain crops are prevented to some extent from this kind of stress by improving varieties tolerant to cold, hot weather or early maturing varieties. As is often the case, control of planting time or harvesting is able to be a good management for escaping the stress. Lodging, plant diseases and pests are considered as a direct or indirect damage due to weather stress, but these are characters able to be overcome by means of crop improvement and also controlled by other suitable methods. In addition, polytical supports capable of improving constitution of agriculture into modern industry is urgently required by programming of data for the damages, establishment of damage forecasting and compensation system.

• Korean Security Journal
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• no.41
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• pp.293-326
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• 2014

Piracy is a worldwide issue, but the deteriorating security situation in the seas off Somalia, the Gulf of Aden and the wider Western Indian Ocean between 2005 and 2012 and in the increasing number of attacks in the Gulf of Guinea are a major problem. The depth of concern for the problem internationally is amply demonstrated by the levels of co-operation and coordination among naval and other forces from several countries that have assembled in the west Indian Ocean region and the Gulf of Aden to escort ships carrying humanitarian aid to Somalia and to protect vulnerable shipping. Notwithstanding this unprecedented effort, the vast sea area in which the pirates now operate makes it difficult to patrol and monitor effectively, particularly with the limited resources available. More resources, in the form of naval vessels and aircraft, are needed and at every opportunity the IMO encourages Member Governments to make greater efforts to provide the additional naval, aerial surveillance and other resources needed through every means possible. IMO provide interim guidance and recommendations to be taken into account when considering the use of PCASP(privately contracted armed security personnel) if and when a flag State determines that such a measure would be lawful and, following a full risk assessment, appropriate. The interim guidance and recommendations of IMO are not intended to endorse or institutionalize the use of armed guards. Therefore, they do not represent any fundamental change of policy by the Organization in this regard. It is for each flag State, individually, to decide whether or not PCASP should be authorized for use on board ships flying their flag. If a flag State decides to permit this practice, it is up to that State to determine the conditions under which authorization will be granted. Therefore, Korea should be introduced rationally PCASP for safe shipping. PCASP on board ships is much the same to special guard personnel of security services industry act. Act plan of Oceans and fisheries ministry on PCASP collides with special guard personnel system of National Police Agency. Rather than new law making, PCASP regukations have to be included in security services industry act. Management Agency of PCASP is to not Oceans and fisheries ministry, but Central Headquarters Korea Coast Guard of Public Safety and Security Ministry because of specialty and closely connection.

• 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.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.40 no.4
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• pp.15-31
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• 2022

This study interpreted the logical validity of the landscape restoration and maintenance patterns of Changgyeonggung Palace, where modern landscapes coexist. The results of the study are as follows; First, the changes in the landscape restoration and maintenance attitude concerning the Changgyeonggung management organization were identified. With the establishment of the Office of the Imperial Garden, an imperial property was nationalized. The Cultural Heritage Managing Department was opened in 1961, and Changgyeonggung Palace were preserved as designated as historical sites in 1963. An environmental purification was implemented by the Changgyeonggung Office as a follow-up measure for restoration in 1983. As the Cultural Heritage Administration promoted in 1999 and the Royal Palaces and Tombs Center was established in 2019, the palace has been managed professionally as a palace landscape to provide a viewing environment. Second, In the 'Purification Period of Changgyeongwon(1954~1977)', environmental purification was carried out to restore amusement facilities, install facilities for cherry blossom viewing, and develop the place into a national zoo. In the 'Reconstruction Period of Changgyeonggung(1983~1986)', restoring function as an urban park, reserving green areas, the outside space was recreated in the traditional feel, and the forest area was generally maintained. In the 'Supplementation Period of Traditional Landscape Architecture Space(1987~2009)', a uniform green landscape was created with pine trees and various vegetation landscapes centered on the flower beds. In the 'Improvement and Maintenance Period of Viewing Environment(2010~2022), a master plan was reestablished on the premise of utilization, but maintenance has been carried out in a small scale centering on unit space. Third, regarding the validity of the landscape restoration and maintenance, It was found in terms of 'originality' that the recovery of the palace system has not been expanded for over 40 years in existing areas. The 'characteristics of the times', which shows whether multi-layered history was taken into account, Changgyeongwon was excluded from the discussion in the process of setting the base year twice. In terms of 'integrity,' the area of the Grand Greenhouse where the historic states coexists needs a maintenance policy that binds the greenhouse, carpet flower bed, and Chundangji Pond. The 'utility' identified as the utilization of spaces suggests the establishment of a sense of place in the Grand Greenhouse area, which is concentrated with programs different from other areas.

• 한국노년학
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• v.39 no.2
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• pp.199-211
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• 2019

The purpose of this study is to analyze leisure satisfaction and leisure performance according to whether elderly people are performing their preferred leisure activities. For the analysis, we used sample from the 6th (2015) panal data as Korean Retirement and Income Study(KReIS). The results of this study were as follows. First, the total data of 4,197 elderly (2,212 young-old and 1,985 old-old) were analyzed. As a result, weekday and weekend leisure time of the old-old (7.64 hours / 7.81 hours) than the young-old (6.83 hour / 7.39 hour) was increased and resting activites (over 70% of watching TV and listening to the radio) accounted for more than 80% of the both elderly leisure activities. Leisure performance were higher in old-old who did not perform preferred leisure activities during weekdays. Leisure performance on weekends was higher in old-old regardless of whether they had preferred leisure time. Average of leisure performance was high in both groups and they responded leisure satisfaction was moderate. In the case of need for leisure change, young-old was higher than oid-old regardless of preference leisure performance and day of the week. However, the responses of the both groups are closed to those that do not want to change. Based on the results of this study, it should be practiced such as develomenting program and introduction of health management system considering leisure constraints to improve leisure satisfaction and continuance of leisure activities for young-old and old-old. We also emphasize the need for a systematic survey scale that takes into account the qualitative aspects of leisure activities as well as the subjective factors influencing leisure participation.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.