• Knowledge Management Research
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• v.20 no.3
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• pp.155-171
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• 2019

In the entertainment industry which has great uncertainty, it is essential to predict public preference first. Thanks to various mass media channels such as cable TV and internet-based streaming services, the reality audition program has been getting big attention every day and it is being used as a new window to new entertainers' debut. This phenomenon means that it is changing from a closed selection process to an open selection process, which delegates selection rights to the public. This is characterized by the popularity of the public being reflected in the selection process. Therefore, this study aims to implement a machine learning model which predicts the winner of , which has recently been popular in South Korea. By doing so, this study is to extend the research method in the cultural industry and to suggest practical implications. We collected the data of winners from the 1st, 2nd, and 3rd seasons of the Produce 101 and implemented the predictive model through the machine learning method with the accumulated data. We tried to develop the best predictive model that can predict winners of by using four machine learning methods such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Neural Network. This study found that the audience voting and the amount of internet news articles on each participant were the main variables for predicting the winner and extended the discussion by analyzing the precision of prediction.

• Journal of dental hygiene science
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• v.22 no.1
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• pp.30-36
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• 2022

Background: Nowadays, dental health problems in Indonesia are still quite high. It is one of which influenced by low public awareness of the importance of maintaining the health of teeth and mouth that can be measured by toothbrushing behavior. Based on the results of RISKESDAS 2018, only 2.8 percent of the population has a proper toothbrushing behavior. Behavior tends to form at age 6 to 12 years. At this age, children begin to develop habits that tend to settle until adulthood, including toothbrushing behavior. Social cognitive theory is a theory of behavioral change that explains that behavioral changes are influenced by the environment, personal, behavior where these three factors influence each other. This study aims to identify changes in the dental behavior of second grades students before and after the joint toothbrushing at school for 21 days. Methods: A pre-experimental study-design was conducted on elementary school by pre-post treatment method where there are 2 classes that get intervention and 2 other classes as control. A joint toothbrush is performed every morning before the school activities begin. Before and after the joint toothbrushing, all classes are given questionnaires to see if there are any changes in behavior seen through knowledge, attitudes, and practice. Results: Respondent group showed increasement on their knowledge, attitudes, and behaviors towards toothbrushing. In contrast, the control groups showed no significant differences in the 3 factors. Conclusion: In this study the education of toothbrushing through environmental changes is quite effective in elementary school children. Insights into the benefits of this program and refinements of optimally targeted intervention, including longitudinal studies are needed to improve the results.

• Journal of Korean Academy of Nursing
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• v.38 no.6
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• pp.822-830
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• 2008

Purpose: The purpose of this study was to analyze effects of a community-based case management program for clients with hypertension living in the community. Methods: The research design was a one group pre and post-test design with 30 participants with hypertension who agreed to participate in the 8-12 week case management program provided by case managers from the National Health Insurance Corporation in 2002. Data were collected three times, before and after the case management services, and 6 months later. Outcomes included changes in blood pressure, knowledge of hypertension and daily life practices, including alcohol consumption, smoking, exercise, and medication adherence. Results: Repeated-measures ANOVA and post-hoc tests of means revealed significant differences before and after service for systolic blood pressure, daily life practices (monitoring body weight and BP, low salt and cholesterol and high vegetable diet, and stress-relief practices), and exercise. The goal for medication adherence was attained after service. Significant improvements from baseline to 6 months after service were observed in measures of salt and vegetables in diet. There were no significant differences on hypertension knowledge, alcohol consumption or smoking behavior between before service and after, and at 6 months. Conclusion: The findings provide preliminary evidence that case management intervention can have positive outcomes on BP control, daily life practices, exercise, and medication adherence for clients with hypertension. However, additional interventions are needed to sustain long-term effects.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• Korean Journal of Community Nutrition
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• v.22 no.4
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• pp.307-322
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• 2017

Objectives: This study examined the effects of nutrition education focused on personalized daily energy requirement and food units using Food Exchange System on anthropometric, biochemical characteristics, nutrition knowledge, dietary attitude and nutrient intakes for overweight and obese in a public health center. Methods: The subjects were 60 overweight/obese women based on BMI (educated 30 vs. non-educated 30, 50~64 years). Educated group was provided individual and/or group lessons (40 min/lesson/week, 5 week), 'Introduction: obese & health', '6 nutrients and 6 food groups', 'My obesity & daily needed energy', 'Meal planning for personalized daily energy and food units using Food Exchange Systems', and 'Smart food choices'. After education, we examined the differences in anthropometric/biochemical characteristics, nutrition knowledge, dietary attitude and nutrient intakes between educated group and non-educated group. Results: After nutrition education, in the educated group, there were improvements on anthropometric/biochemical characteristics, nutrition knowledge, dietary attitude and nutrient intakes in the educated group compared to the non-educated group. We observed a decrease in the mean weight, total cholesterol (TC) and the incidence of overweight/obesity and hypercholesterolemia and an increase in the mean lean body mass. The scores of nutrition knowledge, 'Function of carbohydrate, protein, vitamin, mineral' and 'Food Sources of fat, vitamin, mineral' were increased. The scores of dietary attitudes, 'Taking a joyful meal, a leisurely meal, a balanced meal, a meal with sufficient vegetables, a meal with diversity, a meal with spicy foods, a meal with overeating' were increased. The intakes of energy, carbohydrate, fat, protein, vitamin A, thiamin, Zn and cholesterol were decreased. The scores of INQ, protein, vitamin A, vitamin C, thiamin, riboflavin, vitamin B6, folate, Ca, P, Fe, Zn were increased. Conclusions: The nutrition education focused on personalized daily energy requirement and food exchange unit using Food Exchange System for overweight and obese may improve food behavior, dietary intakes and symptoms of overweight and obese, even in a community health center.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.319-329
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• 2021

This study aims to investigate Korean fashion-related research trends and knowledge structures on digital transformation through information-based approaches. Accordingly, we first identified the current status of the relevant research in Korean academic literature by year and journal; subsequently, we derived key research topics through network analysis, and then analyzed major research trends and knowledge structures by time. From 2010 to 2020, we collected 159 studies published on Korean academic platforms, cleansed data through Python 3.7, and measured centrality and network implementation through NodeXL 1.0.1. The results are as follows: first, related research has been actively conducted since 2016, mainly concentrated in clothing and art areas. Second, the online platform, AR/VR, appeared as the most frequently mentioned topic, and consumer psychological analysis, marketing strategy suggestion, and case analysis were used as the main research methods. Through clustering, major research contents for each sub-major of clothing were derived. Third, major subject by period was considered, which has, over time, changed from consumer-centered research to strategy suggestion, and design development research of platforms or services. This study contributes to enhancing insight into the fashion field on digital transformation, and can be used as a basic research to design research on related topics.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.2
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• pp.101-123
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• 2021

This study analyses the questions and answers contained in the Knowledge Information Database of the collaborative digital reference service, 'Ask a librarian'. And based on the results of status of user requests, this study draws information usage behavior in the early stages of the service was derived. 1,124 Knowledge Information Database items out of 3,506 cases was analyzed by nine criterion. ① Number of questions and whether to be reference questions, ② Subject and keywords of the question, ③ Purpose of the question, ④ Type of question, ⑤ User's information request, ⑥ Information source and reference services provided by the librarian, ⑦ Number of days to answer, ⑧ Level of the participating library, ⑨ Question type by topic. As a results of analysis, first, users asked for reference questions from various topics as needed, rather than one from a similar topic at a time, but more than half of the total pure reference questions were from the field of library information science. Second, about 71.35% of users were using the 'Ask a librarian' service to recommend a list of information resources related to a particular topic or research problem, and there were also questions that required consultation on the reading situation. Third, the most preferred sources of information for users were bibliography, and in the case of online information sources, users did not relatively prefer them. Fourth, the number of days required to answer was able to confirm significant differences depending on the type of question and the level of the participating library. Fifth, 31.33% of the purpose of the general field question showed that were self-generated.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.103-118
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• 2014

Over the last couple of years, traditional VANET (Vehicular Ad Hoc NETwork) evolved into VANET-based clouds. From the VANET standpoint, applications became richer by virtue of the boom in automotive telematics and infotainment technologies. Nevertheless, the research community and industries are concerned about the under-utilization of rich computation, communication, and storage resources in middle and high-end vehicles. This phenomenon became the driving force for the birth of VANET-based clouds. In this paper, we envision a novel application layer of VANET-based clouds based on the cooperation of the moving cars on the road, called CaaS (Cooperation as a Service). CaaS is divided into TIaaS (Traffic Information as a Service), WaaS (Warning as a Service), and IfaaS (Infotainment as a Service). Note, however, that this work focuses only on TIaaS and WaaS. TIaaS provides vehicular nodes, more precisely subscribers, with the fine-grained traffic information constructed by CDM (Cloud Decision Module) as a result of the cooperation of the vehicles on the roads in the form of mobility vectors. On the other hand, WaaS provides subscribers with potential warning messages in case of hazard situations on the road. Communication between the cloud infrastructure and the vehicles is done through GTs (Gateway Terminals), whereas GTs are physically realized through RSUs (Road-Side Units) and vehicles with 4G Internet access. These GTs forward the coarse-grained cooperation from vehicles to cloud and fine-grained traffic information and warnings from cloud to vehicles (subscribers) in a secure, privacy-aware fashion. In our proposed scheme, privacy is conditionally preserved wherein the location and the identity of the cooperators are preserved by leveraging the modified location-based encryption and, in case of any dispute, the node is subject to revocation. To the best of our knowledge, our proposed scheme is the first effort to offshore the extended traffic view construction function and warning messages dissemination function to the cloud.

• Journal of Society of Preventive Korean Medicine
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• v.11 no.1
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• pp.23-34
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• 2007

Health promotion program utilizing traditional korean medicine(TKM) is very unique modality uncommonly conducted throughout the world. Korea's TKM public health services went through initial stage from 2001 and spread throughout the country by 2003. 35 public health centers in the nation is appointed as 'TKM health promotion HUB' and in operation from 2007. But the program is still in the early state and evaluation of usage, satisfaction, and community service is still in demand. This study aims to verify current situations of TKM public health service by examining public awareness, usage, and satisfaction and suggest improvements based on findings. 2.5% of local residents were sampled and 1739 were chosen as subjects. Knowledge, attitude, satisfaction and others for TKM public health service were examined. TKM treatment service was most widely utilized(13.2%), followed by CVA prevention program(5.2%). For satisfaction level, CVA prevention program showed highest satisfaction(73.3%), closely followed by (Qi-gong program 63%). In general, awareness and utilization of TKM public health service were low but compensated by high satisfaction level. More active promotion and development of community specific programs should become available in the future.