• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.13-24
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• 2022

In order to explore the context of fraudulent claims and the measures for preventing them targeting the long-term care institutions for the elderly, which is increasing every year in Korea, this study conducted the text mining analysis using the media report articles. The media report articles were collected from the news big data analysis system called 'BIG KINDS' for about 15 years from July 2008 when the Long-Term Care Insurance for the Elderly took effect, to February 28^th 2022. During this period of time, total 2,627 articles were collected under keywords like 'elderly care+fraudulent claims' and 'long-term care+fraudulent claims', and among them, total 946 articles were selected after excluding overlapped articles. In the results of the text mining analysis in this study, first, the top 10 keywords mentioned in the highest frequency in every section(July 1^st 2008-February 28^th 2022) were shown in the order of long-term care institution for the elderly, fraudulent claims, National Health Insurance Service, Long-Term Care Insurance for the Elderly, long-term care benefits(expenses), elderly care facilities, The Ministry of Health & Welfare, the elderly, report, and reward(payment). Second, in the results of the N-gram analysis, they were shown in the order of long-term care benefits(expenses) and fraudulent claims, fraudulent claims and long-care institution for the elderly, falsehood and fraudulent claims, report and reward(payment), and long-term care institution for the elderly and report. Third, the analysis of TF-IDF was similar to the results of the frequency analysis while the rankings of report, reward(payment), and increase moved up. Based on such results of the analysis above, this study presented the future direction for the prevention of fraudulent claims of long-term care institutions for the elderly.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.115-124
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• 2022

The purpose of this study is to analyze and present keywords, trends, and language networks of keywords for each year of the pedagogical exam of the secondary teacher's employment exam for the 2019~2022 school year. The main research methods were text mining technique and language network analysis method, and analysis programs were KrKwic, Wordcloud Maker, Ucinet6, NetDraw, etc. The research results are as follows; First, keywords such as teacher, student, curriculum, class, and evaluation appeared in the top rankings, and keywords (online, wiki, discussion ceremony, information, etc.) that reflect the recent online class progress in the current COVID-19 situation also tended to appear. The keywords with high frequency of occurrence in the four-year integrated text were student(44), teacher(39), class(27), school(18), curriculum(16), online(10), and discussion method(8). Second, the overall language network of the keywords with high frequency of 4 years showed a significant level of density(0.566), total number of links(492), and average degree of links(16.4). The degree centrality was found in the order of teacher(199.0), class(197.0), student(185.0), and school(150.0). Betweenness centrality was found in the order of teacher(30.859), class(18.956), student(16.054), and school (15.745). It is expected that the results of this study will serve as data to be considered for preparatory teachers, institutions and related persons, and teachers and administrators of secondary school teacher training institutions.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.89-102
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• 2024

This study aims to examine the characteristics of the user experience of New Town neighborhood parks and explore issues that diversify the experience of the parks. In order to quantitatively analyze a large amount of park visitors' experiences, text-based Naver blog reviews were collected and analyzed. Among the Phase 1 and 2 New Towns, the parks with the highest user experience postings were selected for each city as the target of analysis. Blog text data was collected from May 20, 2003, to May 31, 2022, and analysis was conducted targeting Ilsan Lake Park, Bundang Yuldong Park, Gwanggyo Lake Park, and Dongtan Lake Park. The findings revealed that all four parks were used for everyday relaxation and recreation. Second, the analysis underscores park's diverse user groups. Third, the programs for parks nearby were also related to park usage. Fourth, the words within the top 20 rankings represented distinctive park elements or content/programs specific to each park. Lastly, the results of the network analysis delineated four overarching types of park users and the networks of four park user types appeared differently depending on the park. This study provides two implications. First, in addition to the naturalistic characteristics, the differentiation of each park's unique facilities and programs greatly improves public awareness and enriches the individual park experience. Second, if analysis of the context surrounding the park based on spatial information is performed in addition to text analysis, the accuracy of interpretation of text data analysis results could be improved. The results of this study can be used in the planning and designing of parks and greenspaces in the Phase 3 New Towns currently in progress.

• Korean Journal of Comparative Education
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• v.27 no.4
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• pp.187-209
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• 2017

The purpose of this study was to highlight the necessity of a conceptual reestablishment of world university evaluations. The hitherto most well-known and validated world university evaluation systems such as Times Higher Education (THE), Quacquarelli Symonds (QS) or Academic Ranking of World Universities (ARWU) primarily assess big universities with quantitative evaluation indicators and performance results in the rankings. Those Systems have instigated a kind of elitism in higher education and neglect numerous small or local institutions of higher education, instead of providing stakeholders with comprehensive information about the real possibilities of tertiary education so that they can choose an institution that is individually tailored to their needs. Also, the management boards of universities and policymakers in higher education have partly been manipulated by and partly taken advantage of the elitist ranking systems with an economic emphasis, as indicated by research-centered evaluations and industry-university cooperation. To supplement such educational defects and to redress the lack of world university evaluation systems, a new system called 'U-Multirank' has been implemented with the financial support of the European Commission since 2012. U-Multirank was designed and is enforced by an international team of project experts led by CHE(Centre for Higher Education/Germany), CHEPS(Center for Higher Education Policy Studies/Netherlands) and CWTS(Centre for Science and Technology Studies at Leiden University/Netherlands). The significant features of U-Multirank, compared with e.g., THE and ARWU, are its qualitative, multidimensional, user-oriented and individualized assessment methods. Above all, its website and its assessment results, based on a mobile operating system and designed simply for international users, present a self-organized and evolutionary model of world university evaluation systems in the digital and global era. To estimate the universal validity of the redefinition of the world university evaluation system using U-Multirank, an epistemological approach will be used that relies on Edgar Morin's Complexity Theory and Karl Popper's Philosophy of Science.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.325-332
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• 2011

This research was conducted to analyze the effects of raising farm on the heritability and breeding values of Hanwoo cows for their carcass traits, including cold carcass weight (CWT), back-fat thickness (BFT), eye-muscle area (EMA) and marbling score (MAR). The carcass data and pedigree data were collected from steers raised on Hanwoo farms in Pyeongchang-gun, Gangwon-do, South Korea. Three analytical models were applied for the estimation of heritabilities and breeding values. The first model (model 1) included slaughter house-year-month combination as fixed effects and age at slaughter was fitted as linear and quadratic covariates. The second model (model 2) was similar to model 1, but raising farm was additionally included as random effect. The third model (model 3) was similar to model 1 but farm effects were additionally included as fixed effect. The comparisons between the model 1 and the models including farm effect (model 2 and model 3) revealed that heritability estimates from model 2 or model 3 were smaller to those from model 1 for all carcass traits. Especially, obvious decrease of heritability was observed in CWT where heritability was 0.23 from model 1, 0.15 from model 2 and 0.18 from model 3. The maximum log likelihood of the model 2 and 3 were higher than those of model 1 for all traits. In model 2 that raising farm was included as a random effect, the ratio of farm variance to the total phenotypic variance were ranged from 4% (EMA) to 18% (CWT). Top 10% and bottom 10% of female cows were selected based on the breeding values from model 1, and the Spearman's rank correlation coefficients among models were estimated for each trait within selected group. The correlation coefficients were ranged from 0.57 to 0.95 in top 10% group and from 0.68 to 0.95 in bottom 10% group. These results show that the discrepancies in the rankings of breeding values can be based on the models applied. In conclusion, the results obtained in this study suggest that the herd effect or farm effect should be included in the analytical model when breeding values are estimated with the purpose of improvement of carcass traits of Hanwoo breeding cows.

• Journal of Nutrition and Health
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• v.50 no.1
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• pp.98-110
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• 2017

Purpose: The purpose of this study was to evaluate the dietary potassium intake, Na/K intake molar ratio, consumption of 18 food groups, and foods contributing to potassium intake of Korean adults as well as the relationships among quartile of potassium intake level and blood pressure, blood biochemical index. Methods: This study was conducted using the Korea National Health and Nutrition Examination Survey, 2007~2010. The total number of subjects was 20,291. All analyses were conducted using a survey weighting to account for the complex survey design. Results: Overall average intakes of potassium were 2,934.7, 3,070.6, 3,078.1, and 3,232.0 mg/day, and they significantly increased by year in Korean adults. The average dietary potassium intake was close to adequate intake (AI), whereas that of women was considerably lower than the AI. The Na/K intake molar ratio in males (2.89~3.23) was higher than in females (2.62~2.95). The major food groups contributing to potassium intake were vegetables, cereals, and fruits/meats. The two major foods contributing to potassium intake were polished rice and cabbage kimchi. The rankings of food source were as follows; polished rice > cabbage kimchi > potato > oriental melon > sweet potato > seaweed > radish > apple > black soybean. In 50~64 year old females, systolic blood pressure (SBP) significantly decreased (p < 0.01) and HDL-cholesterol significantly increased (p < 0.05) as potassium intake increased. Triglyceride (TG) was significantly higher in the other quartile of potassium intake level than in the first quartile (p < 0.05). Conclusion: In conclusion, our study suggests the need for an appropriate set of dietary reference intakes according to caloric intake by sex and age groups and for development of eating patterns to increase potassium intake and decrease sodium intake.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.

• Journal of Korean Society of Forest Science
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• v.111 no.1
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• pp.125-135
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• 2022

In this study, growth changes of the diameter at breast height (DBH), height, basal area, volume, and biomass of Korean white pine (Pinus koraiensis Siebold & Zucc.) on a plantation were examined via long-term monitoring. In addition, this study was performed to provide the basic data for timber production in line with DBH class by comparing the growth of the relative DBH size. Growth characteristics according to DBH class were analyzed by categorizing trees into five classes based on sorted DBH rankings: class I (1%-20%; upper 20%), class II (21%-40%), class III (41%-60%), class IV (61%-80%), class V (81%-100%; lower 20%). A total class (0%-100%) was also used. Total increment and mean annual increment (MAI) were calculated using data from nine measurements taken over 39 years. Tree characteristics based on average values and stand characteristics based on unit area per hectare were examined. According to the total increments of variables, the differences in DBH, basal area, volume, and biomass among classes I-V increased over time, whereas the height difference did not continually increase. According to MAI, the maximum DBH value was 0.92 cm·yr^-1 at age 23 in class I, whereas the maximum value in all trees was 0.69 cm·yr^-1 at age 17. The maximum value of height MAI for class I was 0.52 m·yr^-1 at age 23, whereas that for all trees was 0.49 m·yr^-1 at age 20. In terms of basal area, volume, and biomass growth at tree-and stand-level, the maximum MAI of class I and all trees was not observed during the measurement period. Therefore, additional long-term monitoring data are required to determine the maximum MAI of the variables.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.