• Journal of the Society of Disaster Information
• /
• v.20 no.2
• /
• pp.462-469
• /
• 2024

Purpose: This study aims to analyze data on disaster safety education for migrant youth and to examine the corresponding social perceptions. Method: Data on disaster safety education for migrant youth were collected and analyzed using Textom and Ucinet. The data used in the study were searched on portal websites from 2016 to 2023 using the keywords 'migrant youth+ disaster + safety education'. Result: The analysis results showed that 'education (306)' had the highest frequency, followed by 'safety (287)', 'school (97)', 'society (85)', and 'support (77)'. The keyword with the high degree of centrality, closeness centrality, and betweenness centrality were 'education', 'safety' and 'society'. 'Family' ranked higher in betweenness centrality than the rankings of frequency analysis, degree centrality and closeness centrality, indicating that 'family' plays a significant role as a mediator in the network of disaster safety education for migrant youth. Conclusion: By examining social awareness about disaster safety education for migrant youth, the findings will be used to develop policies and strategies for disaster safety education that consider the unique vulnerabilities of migrant youth in disaster situations.

• Knowledge Management Research
• /
• v.24 no.4
• /
• pp.1-22
• /
• 2023

Knowledge management can be defined as the valuable storing and creation of new knowledge, as well as the sharing of this knowledge to be applied in all areas of an organization's management activities (Turban et al., 2003). In a knowledge-based society where intangible intellectual assets are the source of competitive advantage rather than tangible assets, knowledge management activities are emphasized in both academia and industry. This study analyzes research on "knowledge management" keyword indexed in the Korean Citation Index (https://kci.go.kr), operated by the National Research Foundation of Korea (NRF), to identify related research trends in Korea and suggest future directions for knowledge management activities. The results show that knowledge management is being researched through the integration of various fields and theories, indicating the potential for expanding research topics and fostering interdisciplinary collaboration. Furthermore, the study of knowledge management often include the keyword 'innovation', emphasizing its significant role in organizational and technological innovations. The analysis of keywords by year also reveals that they reflect the major environmental changes of each period, demonstrating the increasing importance of knowledge management in the era of the Fourth Industrial Revolution.

• Clinical Nutrition Research
• /
• v.12 no.1
• /
• pp.65-76
• /
• 2023

The present systematic review and meta-analysis were accomplished to understand the effects of tart cherry juice consumption on body composition and anthropometric measures. Five databases were searched using relevant keywords from inception to January 2022. All clinical trials investigating the effect of tart cherry juice consumption on body weight (BW), body mass index (BMI), waist circumference (WC), fat mass (FM), fat-free mass (FFM), and percentage body fat (PBF) were included. Out of 441 citations, 6 trials that enrolled 126 subjects were included. Tart cherry juice consumption significantly did not reduce BW (weighted mean difference [WMD], -0.4 kg; 95% confidence interval [CI], -3.25 to 2.46; p = 0.789; GRADE = low), BMI (WMD, -0.07 kg/m²; 95% CI, -0.89 to 0.74; p = 0.857; GRADE = low), FM (WMD, 0.21 kg; 95% CI, -1.83 to 2.25; p = 0.837; GRADE = low), FFM (WMD, -0.12 kg; 95% CI, -2.47 to 2.27; p = 0.919; GRADE = low), WC (WMD, 1.69 cm; 95% CI, -1.88 to 5.27; p = 0.353; GRADE = low), and PBF (WMD, 0.18%; 95% CI, -1.81 to -2.17; p = 0.858; GRADE = low). Overall, these data suggest that tart cherry juice consumption has no significant effect on BW, BMI, FM, FFM, WC, and PBF.

• Herbal Formula Science
• /
• v.31 no.4
• /
• pp.373-388
• /
• 2023

Objectives : This study aimed to provide basic data for research by investigating non-clinical experimental studies on herbal medicines and its compounds for precocious puberty. Methods : A search was conducted for all literature until October 2023 using combinations of keywords such as precocious puberty, puberty, and chinese medicine in three databases (Pubmed, OASIS, and ScienceON). Results : 1. In animal experiments, studies were mainly conducted using a model that induced precocious puberty by subcutaneously administering danazol to SD rats on the 5th day after birth, and in cell experiments, precocious puberty was induced by treating GT1-7 cells with kisspeptin 10 or estradiol. 2. Anemarrhenae Rhizoma, Phellodendri Cortex, and Prunellae Spica were mainly used as herbal medicine to evaluate their efficacy on precocious puberty in non-clinical experiments. 3. Macroscopic observation, hematological analysis, histological analysis, and genetic analysis were performed as methods to analyze the experimental results. Conclusions : In this study, the effects of herbal medicine on precocious puberty and non-clinical research methods were confirmed. Based on the results of this study, it is expected that non-clinical effectiveness and mechanism research on materials that are clinically effective in Traditional Korean Medicine will be revitalized.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.119-138
• /
• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.25-44
• /
• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.305-313
• /
• 2021

This study collected and analyzed big data from 1995 to 2020, focusing on the keywords "unwed mother", "single mother," and "single mom" to present appropriate government support policy directions according to changes in perspectives on unwed mothers. Big data collection platform Textom was used to collect data from portal search sites Naver and Daum and refine data. The final refined data were word frequency analysis, TF-IDF analysis, an N-gram analysis provided by Textom. In addition, Network analysis and CONCOR analysis were conducted through the UCINET6 program. As a result of the study, similar words appeared in word frequency analysis and TF-IDF analysis, but they differed by year. In the N-gram analysis, there were similarities in word appearance, but there were many differences in frequency and form of words appearing in series. As a result of CONCOR analysis, it was found that different clusters were formed by year. This study confirms the change in the perspective of unwed mothers through big data analysis, suggests the need for unwed mothers policies for various options for independent women, and policies that embrace pregnancy, childbirth, and parenting without discrimination within the new family form.

• The Journal of the Korea Contents Association
• /
• v.20 no.8
• /
• pp.19-33
• /
• 2020

This study aims to analyze on the changes of opinion in terms of Sejong City construction using big data. The research data are newspaper articles related to the argument of construction in Sejong City. The newspaper article data was reported by Hankyoreh, Dong-A Ilbo and Hankook Ilbo. The arguments related to the construction of Sejong City was included the new administrative capita, multifunctional administrative city and amendments of Sejong City. The analysis method used in this study is frequency analysis, sentiment analysis and social network analysis using python and gephi 0.9.2 programs. The results of the analysis are as follows. First, as a result of frequency analysis, the keywords of Hankyoreh showed the characteristics of consent - consent - dissent according to the construction period of Sejong City. The Dong-A Ilbo showed positions of dissent - dissent - consent. In addition, the Hankook Ilbo was analyzed to have the characteristics of dissent - consent - dissent tendency. Secondly, results of sentiment analysis, The Hankyoreh showed positive - positive - negative tone. The characteristic of Dong-A Ilbo is that the focus has changed from negative to negative to positive. The Hankook Ilbo showed that changed from negative to positive to negative. Finally, the results of social network analysis are as follows. At the time of the construction of Sejong City, each opinion of media was showed a changes in issues according to political and ideological characteristics such as conservative, progressive and moderation. In detail, Hankyoreh focused on balanced regional development. The Dong-A Ilbo represented the opinion of the Conservative Party. The Hankook Ilbo was highlighting the issues confronting the conservative party and progressive party during the construction of Sejong City.

• Journal of the Korean Society of Earth Science Education
• /
• v.10 no.3
• /
• pp.290-307
• /
• 2017

The purpose of this study is to analyze the research trends related to qualitative research on science education, to provide basic data of qualitative research on science education and to select the direction of follow-up research. The subject of the study is the level of Korean Citation Index (KCI-listed, KCI listing candidates), that can be searched by the key phrase, 'qualitative research', 'science education' in Korean language through the RISS service. In this study, the Descriptive Statistical Analysis Method is utilized to discover the number of research articles, classifying them by year and by journal. Also, the Sementic Network Analysis was conducted to the frequency of key words, Centrality Analysis throughout a variety of research articles using krkwic and Ucinet6.0. The results show that first, 138 research papers were published in 14 journals from 2005 to 2017. Second,, the analysis showed the highest frequency of appearance keyword in each article, 'elementary school teacher', 'gifted student', 'science teacher', 'class' were higher than others. third, according to the results of the whole Network Analysis, 'Analysis', 'elementary school', 'class' were analyzed as a highly influential node. And 'Comparison', 'inquiry', 'recognition', 'gifted students' were not close to the center of network. Fourth, keywords that appear in all sections are analysis, gifted students, and elementary school students, and can be analyzed continuously based on studies, lessons or recognition, and characteristics. Based on the results of this study, we explored the past and present of the study subjects related to the study of science education quality and discussed future direction of study.

• Asian Pacific Journal of Cancer Prevention
• /
• v.14 no.6
• /
• pp.3979-3985
• /
• 2013

Background: Although primary malignant CNS tumors are registered in the national cancer registry (NCR) of Iran, there are no available data on the incidence of the primary malignant or benign CNS tumors and their common histopathologies in the country. This study analyzed the 10-year data of the Iranian NCR from March 21, 2000 to March 20, 2010, including a systematic review. Materials and Methods: The international and national scientific databases were searched using the search keywords CNS, tumor, malignancy, brain, spine, neoplasm and Iran. Results: Of the 1,086 primary results, 9 papers were selected and reviewed, along with analysis of 10-year NCR data. The results showed that primary malignant brain tumors have an overall incidence of 2.74 per 100,000 person-years. The analysis of the papers revealed a benign to malignant ratio of 1.07. The most common histopathologies are meningioma, astrocytoma, glioblastoma and ependymoma. These tumors are more common in men (M/F=1.48). Primary malignant spinal cord tumors constitute 7.1% of the primary malignant CNS tumors with incidence of 0.21/100,000. Conclusions: This study shows that CNS tumors in Iran are in compliance with the pattern of CNS tumors in developing countries. The NCR must include benign lesions to understand the definitive epidemiology of primary CNS tumors in Iran.