• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.373-384
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• 2014

Data mining is to clarify pattern or correlation of mass data of complicated structure and to predict the diverse outcomes. This technique is used in the fields of finance, telecommunication, circulation, medicine and so on. In this paper, we selected risk factors of respiratory diseases in the field of medicine. The data we used was divided into respiratory diseases group and health group from the Gyeongsangbuk-do database of Community Health Survey conducted in 2012. In order to select major risk factors, we applied data mining techniques such as neural network, logistic regression, Bayesian network, C5.0 and CART. We divided total data into training and testing data, and applied model which was designed by training data to testing data. By the comparison of prediction accuracy, CART was identified as best model. Depression, smoking and stress were proved as the major risk factors of respiratory disease.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1356-1363
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• 2006

The main objective of this research is an increased understanding of how a supplier can successfully manage its care business in the mobile telecommunications market. In order to carry out this purpose, Uzbekistanis mobile telecommunication market has been studied as a case study in this research. The study tried to identify the forces and the factors present in CRM, and the role of these in enhancing (endangering) business. The objective was to gain a better understanding of how customer could be successfully managed and treated through CRM system. Processes and measures of customer satisfaction and loyalty provide two main aspects of the study. The importance of the concept of care and the actions that define it were found to be critically important for creating loyal customers. The relation between customer's needs, satisfaction, and loyalty, and how these ultimately relate to a providing firm's profitability, were seen to be linked in complex ways. The complexity can be studied in many ways but herein the customer satisfaction-loyalty of each event was first evaluated separately. Customer satisfaction and loyalty were then related to each other in order to compare the separate and combined characteristics.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.99-104
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• 2021

The purpose of the study is to examine the impact of self-esteem and unstable adult attachment on compensatory consumption among millennials. The survey was conducted on millennials (19~34) in June, 2018. The results revealed the static relationship between self-esteem and compliment-type compensatory consumption, while the relationship of self-esteem with consolation-type compensation consumption become insignificant when unstable adult attachment was controlled. Unstable adult attachment(anxiety/avoidance) showed significant impace on both compliment-type and consolation-type compensatory consumption. The research findings imply that compensatory consumption behaviors can be explained with psychological and relational factors among consumers in their early adulthood.

• Journal of Technology Innovation
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• v.30 no.2
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• pp.1-27
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• 2022

In recent years, the autonomous vehicle industry has changed drastically. So the needs and interests in predicting future technologies and market prospects of the autonomous vehicle field have been very increased. However, considering the characteristics of the automotive industry, which has various factors, complex correlation of them and big influence on each other, the study of systematic future forecasting methodologies are urgent and necessary which are applicable to autonomous vehicle industry. In this research, the two methods such as "Field Anomaly Relaxation" and "Multiple Perspective Concept" were analyzed and chosen, which are suitable to automotive industry. By the combination of two methods this research developed and examined the three future scenarios related to core technologies and industry trends. And these scenarios feasibility was verified by experts and evaluation checklist. This research has a contribution that this future scenario forecasting approach can be applied to the industries which have various volatility like the autonomous vehicle industry.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.248-248
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• 2020

토양수분은 증발산, 유출, 침투 등 물수지 요소들과 밀접한 연관이 있는 주요한 변수 중에 하나이다. 토양수분의 정도는 토양의 특성, 토지이용 형태, 기상 상태 등에 따라 공간적으로 상이하며, 특히 기상 상태에 따라 시간적 변동성을 보이고 있다. 기존 토양수분 측정은 토양시료 채취를 통한 실내 실험 측정과 측정 장비를 통한 현장 조사 방법이 있으나 시간적, 경제적 한계점이 있으며, 원격탐사 기법은 공간적으로 넓은 범위를 포함하지만 시간 해상도가 낮은 단점이 있다. 또한, 모델링을 통한 토양수분 예측 기술은 전문적인 지식이 요구되며, 복잡한 입력자료의 구축이 요구된다. 최근 머신러닝 기법은 수많은 자료 학습을 통해 사용자가 원하는 출력값을 도출하는데 널리 활용되고 있다. 이에 본 연구에서는 토양수분과 연관된 다양한 기상 인자들(강수량, 풍속, 습도 등)을 활용하여 머신러닝기법의 반복학습을 통한 토양수분의 예측 가능성을 분석하고자 한다. 이를 위해 시공간적으로 토양수분 실측 자료가 잘 구축되어 있는 청미천과 설마천 유역을 대상으로 머신러닝 기법을 적용하였다. 두 대상지에서 2008년~2012년 수문자료를 확보하였으며, 기상자료는 기상자료개방포털과 WAMIS를 통해 자료를 확보하였다. 토양수분 자료와 기상자료를 머신러닝 알고리즘을 통해 학습하고 2012년 기상 자료를 바탕으로 토양수분을 예측하였다. 사용되는 머신러닝 기법은 의사결정 나무(Decision Tree), 신경망(Multi Layer Perceptron, MLP), K-최근접 이웃(K-Nearest Neighbors, KNN), 서포트 벡터 머신(Support Vector Machine, SVM), 랜덤 포레스트(Random Forest), 그래디언트 부스팅 (Gradient Boosting)이다. 토양수분과 기상인자 간의 상관관계를 분석하기 위해 히트맵(Heat Map)을 이용하였다. 히트맵 분석 결과 토양수분의 시간적 변동은 다양한 기상 자료 중 강수량과 상대습도가 가장 큰 영향력을 보여주었다. 또한 다양한 기상 인자 기반 머신러닝 기법 적용 결과에서는 두 지역 모두 신경망(MLP) 기법을 제외한 모든 기법이 전반적으로 실측값과 유사한 형태를 보였으며 비교 그래프에서도 실측값과 예측 값이 유사한 추세를 나타냈다. 따라서 상관관계있는 과거 기상자료를 통해 머신러닝 기법 기반 토양수분의 시간적 변동 예측이 가능할 것으로 판단된다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.475-481
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• 2023

Softwares, especially like machine learning applications, affect human's life style tremendously. Accordingly, the importance of the cost model for softwares increases rapidly. As cost models, LOC(Line of Code) and M/M(Man-Month) estimates the quantitative aspects of the software. Differently from them, FP(Function Point) focuses on estimating the functional characteristics of software. FP is efficient in the aspect that it estimates qualitative characteristics. FP, however, has a limit for evaluating machine learning softwares because FP does not evaluate the critical factors of machine learning software. In this paper, we propose an extended function point(ExFP) that extends FP to adopt hyper parameter and the complexity of its optimization as the characteristics of the machine learning applications. In the evaluation reflecting the characteristics of machine learning applications. we reveals the effectiveness of the proposed ExFP.

• The Journal of Bigdata
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• v.8 no.2
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• pp.201-222
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• 2023

As the e-Commerce gets increased based on the platform, a lot of small and medium sized sellers have tried to develop the more effective strategies to maximize the profit. In order to increase the profitability, it is quite important to make the strategic decisions based on the range of promotion, discount rate and categories of products. This research aims to develop the business intelligence application which can help sellers of e-Commerce platform make better decisions. To decide whether or not to promote, it is needed to predict the level of increase in sales after promotion. I n this research, we have applied the various machine learning algorithm such as MLP(Multi Layer Perceptron), Gradient Boosting Regression, Random Forest, and Linear Regression. Because of the complexity of data structure and distinctive characteristics of product categories, Random Forest and MLP showed the best performance. It seems possible to apply the proposed approach in this research in support the small and medium sized sellers to react on the market changes and to make the reasonable decisions based on the data, not their own experience.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.133-154
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• 2016

Interdisciplinary approach has been recognized as one of key strategies to address various and complex research problems in modern science. The purpose of this study is to investigate the interdisciplinary characteristics and structure of the field of big data. Among the 1,083 journals related to the field of big data, multiple Subject Categories (SC) from the Web of Science were assigned to 420 journals (38.8%) and 239 journals (22.1%) were assigned with the SCs from different fields. These results show that the field of big data indicates the characteristics of interdisciplinarity. In addition, through bibliographic coupling network analysis of top 56 journals, 10 clusters in the network were recognized. Among the 10 clusters, 7 clusters were from computer science field focusing on technical aspects such as storing, processing and analyzing the data. The results of cluster analysis also identified multiple research works of analyzing and utilizing big data in various fields such as science & technology, engineering, communication, law, geography, bio-engineering and etc. Finally, with measuring three types of centrality (betweenness centrality, nearest centrality, triangle betweenness centrality) of journals, computer science journals appeared to have strong impact and subjective relations to other fields in the network.

• Journal of the Korean earth science society
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• v.22 no.1
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• pp.65-74
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• 2001

To evaluate the influence of meteorological conditions on the performance of DOAS (Differential Optical Absorption Spectroscopy) system, we analyzed the concentrations of three criteria pollutants and relevant environmental parameters measured during 14 month periods between Jun. 1999 and Oct. 2000. According to our study, the performance of DOAS can be sensitively influenced via various manners (such as among different chemicals and/or between different time periods). It turns out that O$_3$ exhibits most frequently the weakest agreement between two systems. When comparison was made among different meteorological parameters, the strongest variability was seen from such ones as windspeed, wind direction, and irradiance. In addition, the absolute differences in measured concentrations between two systems were compared against various environmental parameters by means of linear regression analysis. Results of this analysis indicated that the differences between the two tend to decrease with the increase of such parameters as windspeed. It is thus concluded on the basis of our study that the simultaneous evaluation of meteorological data should be an essential step toward the accurate assessment of pollutant concentration data obtained by DOAS measurement system.

• Asia Marketing Journal
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• v.13 no.1
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• pp.113-141
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• 2011

Companies are competing each other, and as the competitions became higher, consumer's information processing for purchase became more complicated. Consumer confusion problem is getting more serious, but there are still not much considerations on this problem. The purpose of this study is to find out that the consumer confusion can causes consumer's negative emotion(anger). This research studied the mediation effect of negative emotion on the relationship between consumer confusion, which was classified into three categories, and two consequences, word-of-mouth and trust. And also it concentrates on moderating effects of negative affectivity and intolerance of uncertainty in the relationship between consumer confusion and negative emotion. For the empirical study, we carried out a survey targeting consumers who live in the Dae-gu metropolitan area. The specific results of this study are as follows. First, all sub-dimensions of the consumer confusion had a positive effect on anger. Second, anger had a positive effect on word of mouth and on the other hand, anger had a negative effect on trust. Third, negative affectivity had a moderating effects on the links between overload and ambiguity confusion with anger, and intolerance of uncertainty only had a moderating effects on the links between overload confusion and anger.