• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.21-27
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• 2019

Selection of feature pattern gathered from the observation of the RNA sequencing data (RNA-seq) are not all equally informative for identification of differential expressions: some of them may be noisy, correlated or irrelevant because of redundancy in Big-Data sets. Variable selection of feature pattern aims at differential expressed gene set that is significantly relevant for a special task. This issues are complex and important in many domains, for example. In terms of a computational research field of machine learning, selection of feature pattern has been studied such as Random Forest, K-Nearest and Support Vector Machine (SVM). One of most the well-known machine learning algorithms is SVM, which is classical as well as original. The one of a member of SVM-criterion is Support Vector Machine-Recursive Feature Elimination (SVM-RFE), which have been utilized in our research work. We propose a novel algorithm of the SVM-RFE with Q-learning in reinforcement learning for better variable selection of feature pattern. By comparing our proposed algorithm with the well-known SVM-RFE combining Welch' T in published data, our result can show that the criterion from weight vector of SVM-RFE enhanced by Q-learning has been improved by an off-policy by a more exploratory scheme of Q-learning.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.507-514
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• 2018

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.213-222
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• 2016

This study aims to establish a system for the rapid discrimination of Zoysia species using metabolite fingerprinting of FT-IR spectroscopy combined with multivariate analysis. Whole cell extracts from leaves of 19 identified Zoysia japonica, 6 identified Zoysia sinica, and 38 different unidentified Zoysia species were subjected to Fourier transform infrared spectroscopy (FT-IR). PCA (principle component analysis) and PLS-DA (partial least square discriminant analysis) from FT-IR spectral data successfully divided the 25 identified turf grasses into two groups, representing good agreement with species identification using molecular markers. PC (principal component) loading values show that the $1,100{\sim}950cm^{-1}$ region of the FT-IR spectra are important for the discrimination of Zoysia species. A dendrogram based on hierarchical clustering analysis (HCA) from the PCA and PLS-DA data of turf grasses showed that turf grass samples were divided into Zoysia japonica and Zoysia sinica in a species-dependent manner. PCA and PLS-DA from FT-IR spectral data of Zoysia species identified and unidentified by molecular markers successfully divided the 49 turf grasses into Z. japonica and Z. sinica. In particular, PLS-DA and the HCA dendrogram could mostly discriminate the 47 Z. japonica grasses into two groups depending on their origins (mountainous areas and island area). Considering these results, we suggest that FT-IR fingerprinting combined with multivariate analysis could be applied to discriminate between Zoysia species as well as their geographical origins of various Zoysia species.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• The Journal of the Korea Contents Association
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• v.11 no.7
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• pp.437-447
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• 2011

This study will compare and analyze the effect of satisfaction of security needs on life satisfaction according to sociodemographic features and will find the factors of security needs satisfaction and life satisfaction. a total of three humdred questionnaires was distributed to male and female who live in Seoul(Gang-dong, Gang-sue, Songpa-gu and Gand-buk) in Aug. and Sept., 2010 and over nineteen years old. Except making the wrong questionnaires, total questionnaires was sampled from 259 questionnaires using judgment sampling method after selecting. Data analysis be used by SPSSWIN 18.0 Version. The validity and reliability of questionnaires are verified for factorial analysis and reliability analysis and also T test and F test are used for finding for differences of life satisfaction and satisfaction of security needs. And, this study is tested the regression analysis for the effects on the life satisfaction and satisfaction of security. Utilizing CCTV on the Life Satisfaction, this study were drawn the conclusions as following. First, the satisfaction of security needs as demographic characteristics have the part of the difference. the result shows to different psychological needs as educational level at the group less than college graduates. Second, the result of satisfaction of security as demographic characteristics is significantly higher in the male group and life satisfaction as education is significantly higher in more than college graduates. Third, the satisfaction of security needs of citizens through the CCTV effects to life satisfaction. environmental needs and information needs are as high as life satisfaction.

• Proceedings of the Korea Database Society Conference
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• 2001.11a
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• pp.473-505
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• 2001

예측 기관에 따라서 B2B의 시장 규모 및 성장률에 대한 차이는 있지만 B2B 시장이 빠른 속도로 성장하고 있으며, 이 같은 추세는 계속될 것이라는 점에 대한 이견은 없는 편이다. B2B는 기업에게 비용 절감과 시간 절약, 업무 효율성 증대 등의 다양한 효과를 제공해 줄 수 있기 때문에 앞으로도 그 중요성은 더 커질 것으로 예상된다. 그러나 B2B의 중요성 및 성장세와는 별도로 아직까지 B2B에 참여하는 기업들이 큰 효과를 거두지 못하고 있는 것이 사실이다. 이에 따라 많은 학자들과 컨설팅 회사들이 B2B의 모형, 추진 전략, 성공 요인들을 다양한 각도에서 제시하고 있다. 하지만 B2B에 대한 실증적 연구가 부족하여, 기업의 실무자들이 실질적인 도움을 얻기에는 부족한 점이 있기 때문에 B2B의 성공 요인과 추진 전략에 대한 실증적 연구가 절실히 필요하다. 본 연구의 목적은 B2B 유형 중 가장 널리 활용되고 있는 MRO e-marketplace의 성공에 영향을 주는 요인들을 실증적으로 분석하는 것이다. MRO e-marketplace의 성공 요인을 환경적 특성, 제품 특성, B2B 사이트 특성 등 3 그룹으로 분류한 후, 38개 기업에서 수집된 설문지를 분석하여 MRO e-marketplace의 성공 요인을 실증적으로 분석하였다. MRO e-marketplace의 성공 요인들을 요인 분석한 결과, 기업 내부 환경 요인, 기업 외부 환경 요인, 제품 정보 요인, 제품 공급 능력 요인, 사이트 기본 기능 요인, 사이트 편의성 요인, 사이트 보안성 요인 등 총 8개 요인으로 분류되었다. 한편 MRO e-marketplace의 도입 효과를 측정한 비용 절감, 시간 절약, 업무 효율성 증대, 거래 투명성 증대 등의 4개의 문항은 하나의 요인으로 묶여, 이를 MRO e-marketplace 성공으로 정의하였다. MRO e-marketplace의 성공에 영향을 미치는 요인을 찾기 위해, 추출된 8개 요인과 MRO e-marketplace 성공 간의 상관 관계를 분석하였다. 8개 요인 중에서 기업 내부 환경 요인, 제품 공급 능력 요인, 사이트 기본 기능 요인이 MRO e-marketplace의 성공에 영향을 미치는 것으로 나타났다. 마지막으로 MRO e-marketplace 성공 요인들의 상대적 중요도를 파악하기 위해 회귀 분석을 실시하였는데, 참여 기업의 내부 환경 요인이 가장 큰 중요한 것으로 나타났고, 그 다음은 제품 공급 능력 요인과 사이트 기본 기능 요인으로 나타났다. 이 같은 실증적 결과는 MRO e-marketplace나 B2B의 성공을 위해서는 참여 기업의 내부 환경 조성이 매우 중요함을 시사해 준다. 또한 참여 기업의 제품 공급 능력 요인 역시 MRO e-marketplace의 성공에 직접적인 영향을 주기 때문에 공급기업들의 제품 공급 능력을 높이는데 노력해야 한다. 또한 MRO e-marketplace를 운영하는 기업들은 사이트의 기능을 높이는데 많은 노력을 기울여야 한다는 것을 시사하고 있다. MRO e-marketplace의 성공 요인을 실증적으로 분석한 본 연구의 결과는 MRO e-marketplace와 B2B의 추진 전략의 이론적 모형 개발에 유용하게 활용될 수 있을 것이다. 또한 본 연구의 결과는 MRO e-marketplace와 B2B의 성공을 높이기 위한 추진 전략을 수립하는데 유용하게 활용될 수 있을 것으로 기대된다.

• Korean Journal of Social Welfare
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• v.35
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• pp.319-344
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• 1998

The endeavor to reveal reasons and backgrounds of juvenile's being delinquent has been continuing. Most of them, however, are not multi-dimensional and integrative, but one-dimensional which has had just focused on the factor of family or individual. One of the main purposes of this. study is to get implications on practical programs through the ecological-systematic analysis on factors and processes of juvenile delinquency. In this study, region has separated into two, one is of poor and the other is non, and then informations on factors and process of being delinquent were gathered by comparing between them. Eleven hundreds and sixteen cases were sampled from six junior-high schools which have met the purpose of this study. The survey had been committed with structured questionnaire which had been consisted in several variables; personal; familial; school and peer related; delinquent characteristics. Reliability and validity of each variables had been tested through pilot test. Effects of independent variables on dependent variables were analyzed according to the region through path analysis. In the analysis, remarkable differences on the processes of being delinquent had been found and three path models of being delinquent had been made on the basis of those differences. Each of them has shown different effecting patterns of personal, familial, and school and peer related variables on one's degree of delinquency. In Pattern 1, peer related variables have committed more powerful effects on the degree of delinquency than the others have. School related variables, in Pattern 2, commit most striking effects on the dependent variables. The degree of delinquency in Pattern 3 is most strongly effected by familial variables. The limitations that personal behavior oriented approach might be confronted in the field of juvenile delinquency have been proved by these results of this study. These results have given many implications to us on the needs of distinctive and integrative approaches to the problems of juvenile delinquency.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.197-213
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• 2022

In this study, text mining analysis was performed on online big data for recognition and assessment of urban brand I Seoul U. To this end, TEXTOM, a processing program for data acquisition and analysis was used, and the 'I SEOUL U' keyword was selected as an analysis keyword. Keyword analysis shows the keywords associated with I Seoul U to be as follows: First, as a business and marketing term, keywords include pop-up store, gallery, co-branding, (festival, etc.), commodities, private companies and online. Second, as an event-related term, keywords include Han River, tree-planting day, tree planting, Hongdae, Christmas, Mapo, Jung-gu, Sejong University, and festival. Third, as a promotional term, keywords include robotics engineer Dr. Dennis Hong, Government, Art and Korea. In the N Gram analysis, as the city brand of Seoul, I Seoul U, in the public interest, was found to contribute to the commercial activities of private companies. In connection-oriented analysis, business and marketing, events, and promotions have been derived as categories. In matrix analysis, it was found that the products of the pop-up store are mainly developed, and products in the form of co-branding were being developed. In the topic modeling, a total of 10 topics were extracted and needs for commercial utilization and information for event festivals were mostly found.

• 지역과문화
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• v.7 no.1
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• pp.153-182
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• 2020

Andong is the place where the most ginger is produced in Korea. The article is based on a study on the development of storytelling of a co-brand of local agricultural products, focusing on the case of 'The Geudae Ginger,' a co-brand of ginger in Andong. This study aims to develop a brand storytelling of Andong Ginger's co-branded 'Geudae Ginger' to build an image as a local specialty and help revitalize the Andong ginger's industry. The process of developing storytelling to activate 'Geudae Ginger' brand is as follows. In the first step, I collected storytelling materials through data research. Ginger, which has long been used as a medicine for mankind, has more historical and cultural stories than anything else. In the second step, story resources were extracted based on data research. By analyzing the story properties of Andong ginger, we made its list. As a result, the image of the nobility, rigidity and chastity of ginger, which is used to benefit all over, could be associated with the image of Andong, the capital of Korean spiritual culture. Storytelling was developed in the third step. The main theme was 'Andong ginger with anther level ' and the main story was 'The Story of Andong's Ginger Teacher'. The scenario developed is as follows: 1. Introducing Andong's Ginger Teacher, 2. The birth of Dosan Thirteen Tea, 3. 'Geudae Ginger' that bridges love. In the last fourth step, I proposed ways to utilize storytelling. I presented the spread methods of consumer-participated storytelling using images of 'Geudae Ginger' and a new-tro event with teachers highlighting the image of 'Ginger Teacher' and others as a local business program for storytelling expansion.

• Journal of Practical Agriculture & Fisheries Research
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• v.21 no.2
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• pp.103-114
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• 2019

In this study we examined the preferences of college life factors for students at Korea National College of Agriculture and Fisheries(KNCAF). Analytical techniques of unstructured data used opinion mining and text mining techniques, and the results of text mining were visualized as word cloud. And those results were used for statistical analysis of the students' willingness to farm after graduation. The items of the favorable survey consisted of 10 items in 5 areas including university image, self-capacity, dormitory, education system, and future vision. After classifying the emotions of positive and negative in the collected questionnaire, a dictionary of positive and negative was created to evaluate the preference. The items of 'college image' at the time of university support, 'self after 10 years' after graduation, 'self-capacity' and 'present KNCAF' showed high positive emotion. On the other hand, positive emotion was low in the items of 'college dormitory', 'educational course', 'long-term field practice' and 'future of Korean agriculture'. In the cross-analysis of the difference in the will to farming according to gender, farming base, and entrance motivation, the will to farm according to gender and entrance motivation showed statistically significant results, but it was not significant in farming base. Also in binary logistic regression analysis on the will to farming, the statistically significant variable was found to be 'motivation for admission'