• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• The Journal of Korean Academic Society of Nursing Education
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• v.3
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• pp.99-116
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• 1997

In response of the increasing demand for professional degree in nursing, some university in Korea offers RN-BSN program for R. N. from diploma in nursing. However, RN-BSN program in Korea is in formative period. Therefore, the purpose of this survey study is for the comparative analysis of RN-BSN curriculum in Korea and U.S.A. In this study, subjects consisted of 18 department of nursing in university and 5 RN-BSN programs in Korea and 18 department of nursing in university and 12 RN-BSN programs in U.S.A. For earn the degree of Bachelor of Science in Nursing, the student earns 134 of mean credits in U.S.A., whereas 150.3 of mean credits in Korea. The mean credit for clinical pratice is 30.1 in U.S.A., whereas 23.9 in Korea. Students are assigned to individually planned clinical experiences under the direction of a preceptor in U.S.A. In RN-BSN program, total mean credits through lecture and clinical practice for earn the degree of BSN is 35.5(lecture : 27.7, practice ; 7.8)in U.S.A., whereas,48.1 (lecture;42.1, practice;6.0) in Korea. RN-BSN program can be taken on a full-or-part time basis in U.S.A., whereas didn't in Korea. Especially, emphasis is place on the advanced nursing practicum that focus on the role of the professional nurse in providing health care to individuals, families, and groups in community setting in U.S.A. 27.7 of mean credits was earned through lecture in U.S.A., whereas 42.1 of mean credits in Korea. It means that RN-BSN program in Korea is the lesser development in teaching method and appraisal method than in U.S.A. Students of RN-BSN program in U.S.A. can earns credit through CLEP, NLN achievement test, portfolio review session etc as well as lecture. Therefore, the authors suggests some recommendations for the development of curriculum of RN-BSN program in Korea based on comparative analysis of RN-BSN curricula in U.S.A. and Korea. 1. The curriculum of RN-BSN Program in nursing was required to do some alterations. Nursing care, today, is complex and ever changing. According to change of public need, RN-BSN curriculum intensified primary care program in community setting, geriatric nursing, marketing skill, computer language. 2. The various and new methods of earning credit should be developed. That is, the students will earn credits through the transfer of previous nursing college credits, accredited examination of university, advanced placement examination, portfolio review session, case study, report, self-directed learning and so on. Flexible teaching place should ile offered. 3. Flexible teaching place should be offered. The RN-BSN curriculum should accommodate each RN student's geographical needs and school/work schedule. Therefore, the university should search a variety of teaching places and the RN students can obtain their degrees comfortably throughout the teaching place such as lecture room inside the health care agency and establishment of the branch school in each student's residence area. 4. The RN-BSN program should offer a long distance education to place-bound RN student in many parts of Korea. That is, from the main office of university, the RN-BSN courses are delivered to many areas by Internet, EdNet (satellite telecommunication) and other non-traditional methods. 5. For allowing RN student to take nursing courses, program length should be various, depending upon the student's study/work schedule. That is, the various term systems such as semester, three terms, quarter systems and the student's status like full time or part time should be considered. Therefore, the student can take advantage of the many other educational and professional opportunities, making them available during the school year.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.6
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• pp.807-815
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• 2012

This study was carried out to investigate the factors affecting the dietary adaptation of female immigrants in multi-cultural families in Busan. The survey was conducted from October 10 to November 30, 2010 using questionnaires, and the data was analyzed using the SPSS program. The subjects were mainly from China (58.8%), Vietnam (14.7%), Philippines (8.8%) and Japan (7.4%). The longer they had resided in Korea, the lower their preference they had for Korean foods. There were no significant differences in preference according to their nationality. The subjects reported that their consumption of cereals (36.7%), meats (40.0%), fish (50.8%), milk and dairy products (47.4%), vegetables (48.3%), fruits (44.8%), fat and oil (29.1%) and beverages (32.8%) were increased after immigration. They usually obtained information about Korean foods from family (26.5%) and television and internet (26.5%). Sixty four point seven percent of the females ate 'everyday' Korean foods and 30.9% ate their home country foods every day. A higher will for learning Korean foods, intake frequency and age resulted in a higher adaptation of Korean dietary life. Lower marital conflict resulted in higher adaptation. These results suggest that it would be effective to provide systematic nutrition education program for female immigrants and their families to adapt to Korean dietary life.

• Korean Security Journal
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• no.52
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• pp.73-91
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• 2017

This study is based on the political and economic standpoint of each country, Use advanced equipment to prevent new terrorism from causing widespread damage, In order to establish a countermeasures against terrorism in accordance with the reality of Korea, which is effective in responding to terrorist attacks, Korea conducted a SWOT analysis of the terrorist environment and terrorist environment through specialists. First, internal strengths of Korea 's terrorist environment include stable security situation, weakness of religious and ethnic conflicts, strong regulation and control of firearms, and counter terrorism capabilities and know - how accumulated during major international events. Second, the internal weaknesses of the terrorist environment in Korea include the insecurity of the people, the instability caused by the military confrontation with North Korea, the absence of anti-terrorism law system, the difficulty of terrorism control and management by the development of the Internet and IT technology. Third, the external opportunities for Korea 's terrorist environment are as follows: ease of supplementation and learning through cases of foreign terrorism failure, ease of increase of terrorist budget and support with higher terrorism issues, strengthening of counterterrorism through military cooperation with allied nationsRespectively. Fourth, the external threats to the terrorist environment in Korea are the increase of social dissatisfaction due to the continuous influx of defectors and foreign workers, the goal of terrorism from international terrorist organizations through alliance with the United States,Increased frequency of incidents, and increased IS coverage of terrorism around the world. In addition, the SWOT in - depth interviews on the terrorist environment of the expert group were conducted to diagnose and analyze the problems, terrorism awareness and legal system in the Korean terror environment. The results of the study are summarized as follows.First, the basic law on terrorism should be enacted.Second, the establishment of an integrated anti-terrorism organization.Third, securing and nurturing specialized personnel in response to terrorism.

• Journal of Korean Home Economics Education Association
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• v.20 no.4
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• pp.157-171
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• 2008

The year of 2007 Reformed Curriculum encourages various activity materials in the textbook facilitate students oriented self-help learning. The purpose of this paper is to find out how much the activity materials in housing area of middle school Technology and Home Economics are practiced in the class and why they are used or not used. The data were collected from 253 middle school teachers who had ever taught the housing unit in any of 6 textbooks. The analyses indicated that the most frequent teaching methode was lecture based on the textbook and internet data focused on the figures and contents of the individual textbook. The average rate of practicing the activity materials was differ by textbooks and the characteristics of the materials such as type of materials, feature of non sentence materials, and type of activity. The main two reasons to practice the activity materials were it's adequacy to class goals and application to everyday life. Low interests of students and shortage of time were the two main reasons why not used the materials. Textbook writers should consider these reasons as well as the characteristics of activity materials practiced in the class by the teachers in order to meet the goals of the reformed as well as current curricula.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.77-92
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• 2014

Recently, numerous documents including unstructured data and text have been created due to the rapid increase in the usage of social media and the Internet. Each document is usually provided with a specific category for the convenience of the users. In the past, the categorization was performed manually. However, in the case of manual categorization, not only can the accuracy of the categorization be not guaranteed but the categorization also requires a large amount of time and huge costs. Many studies have been conducted towards the automatic creation of categories to solve the limitations of manual categorization. Unfortunately, most of these methods cannot be applied to categorizing complex documents with multiple topics because the methods work by assuming that one document can be categorized into one category only. In order to overcome this limitation, some studies have attempted to categorize each document into multiple categories. However, they are also limited in that their learning process involves training using a multi-categorized document set. These methods therefore cannot be applied to multi-categorization of most documents unless multi-categorized training sets are provided. To overcome the limitation of the requirement of a multi-categorized training set by traditional multi-categorization algorithms, we propose a new methodology that can extend a category of a single-categorized document to multiple categorizes by analyzing relationships among categories, topics, and documents. First, we attempt to find the relationship between documents and topics by using the result of topic analysis for single-categorized documents. Second, we construct a correspondence table between topics and categories by investigating the relationship between them. Finally, we calculate the matching scores for each document to multiple categories. The results imply that a document can be classified into a certain category if and only if the matching score is higher than the predefined threshold. For example, we can classify a certain document into three categories that have larger matching scores than the predefined threshold. The main contribution of our study is that our methodology can improve the applicability of traditional multi-category classifiers by generating multi-categorized documents from single-categorized documents. Additionally, we propose a module for verifying the accuracy of the proposed methodology. For performance evaluation, we performed intensive experiments with news articles. News articles are clearly categorized based on the theme, whereas the use of vulgar language and slang is smaller than other usual text document. We collected news articles from July 2012 to June 2013. The articles exhibit large variations in terms of the number of types of categories. This is because readers have different levels of interest in each category. Additionally, the result is also attributed to the differences in the frequency of the events in each category. In order to minimize the distortion of the result from the number of articles in different categories, we extracted 3,000 articles equally from each of the eight categories. Therefore, the total number of articles used in our experiments was 24,000. The eight categories were "IT Science," "Economy," "Society," "Life and Culture," "World," "Sports," "Entertainment," and "Politics." By using the news articles that we collected, we calculated the document/category correspondence scores by utilizing topic/category and document/topics correspondence scores. The document/category correspondence score can be said to indicate the degree of correspondence of each document to a certain category. As a result, we could present two additional categories for each of the 23,089 documents. Precision, recall, and F-score were revealed to be 0.605, 0.629, and 0.617 respectively when only the top 1 predicted category was evaluated, whereas they were revealed to be 0.838, 0.290, and 0.431 when the top 1 - 3 predicted categories were considered. It was very interesting to find a large variation between the scores of the eight categories on precision, recall, and F-score.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.