• Journal of Society of Preventive Korean Medicine
• /
• v.27 no.3
• /
• pp.1-12
• /
• 2023

Objectives : The aim of this study is to analyze the news articles related to Korean Medicine(KM) and compare trends in news reports from 2018 to 2022. Method : News articles related to KM were collected through the BigKinds, the news bigdata service of the Korea Press Foundation. News reports from 1 January 2018 to 31 December 2022 were searched. 2,950 news articles out of a total of 12,497 met the inclusion criteria. First, quantitative changes in media coverage were analyzed by year, media outlet, and month. For qualitative analysis, two authors independently coded the content of news articles, discussed them until consensus, and consulted with a third researcher to classify them. In addition, keywords extracted by the BigKind's Topic Rank algorithm were compared and analyzed in each year. Results : The number of news articles on KM decreased by 42% in 2022 compared to 2018. Over a fiveyear period, the Naeil Shinmun reported the most on KM among newspapers, while the Hankyoreh did the least. Among broadcasters, YTN reported the most and SBS did the least. When analyzing the reports by category, the most common was 'treatment', followed by 'prevention' and 'scientification'. As a result of extracting keywords with high weight and frequency, 'immunity' and 'immune system' ranked the first and second in 2018, while 'COVID 19' and 'medical law violation' did in 2022. Conclusion : The decrease in media reports on KM during the COVID-19 epidemic period seems to be due to the limited role of KM in responding to infectious diseases, and efforts to expand the scope of KM can induce increased media reports and social interest.

• The Journal of Bigdata
• /
• v.4 no.1
• /
• pp.63-72
• /
• 2019

For power energy, optimal generation and distribution plans based on accurate demand forecasts are necessary because it is not recoverable after they have been delivered to users through power generation and transmission processes. Failure to predict power demand can cause various social and economic problems, such as a massive power outage in September 2011. In previous studies on forecasting power demand, ARIMA, neural network models, and other methods were developed. However, limitations such as the use of the national average ambient air temperature and the application of uniform criteria to distinguish seasonality are causing distortion of data or performance degradation of the predictive model. In order to improve the performance of the power demand prediction model, we divided Korea into five major regions, and the power demand prediction model of the linear regression model and the neural network model were developed, reflecting seasonal characteristics through regional characteristics and migration period learning techniques. With the proposed approach, it seems possible to forecast the future demand in short term as well as in long term. Also, it is possible to consider various events and exceptional cases during a certain period.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.37-49
• /
• 2021

The number of dementia patients in Korea is estimated to be over 800,000, and the severity of dementia is becoming a social problem. However, no treatment or drug has yet been developed to cure dementia worldwide. The number of dementia patients is expected to increase further due to the rapid aging of the population. Currently, early detection of dementia and delaying the course of dementia symptoms is the best alternative. This study presented a methodology for early diagnosis of dementia by measuring and analyzing amyloid plaques. This vital protein can most clearly and early diagnose dementia in the retina through AI-based image analysis. We performed binary classification and multi-classification learning based on CNN on retina data. We also developed a deep learning algorithm that can diagnose dementia early based on pre-processed retinal data. Accuracy and recall of the deep learning model were verified, and as a result of the verification, and derived results that satisfy both recall and accuracy. In the future, we plan to continue the study based on clinical data of actual dementia patients, and the results of this study are expected to solve the dementia problem.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.19-33
• /
• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• The Journal of Bigdata
• /
• v.7 no.2
• /
• pp.153-172
• /
• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• Information Systems Review
• /
• v.18 no.2
• /
• pp.199-223
• /
• 2016

As social networking services (SNS) become widely used tools for maintaining social relationships, people use SNS to express themselves online. Users are free to form multiple characters in SNS because of online anonymity. This phenomenon causes SNS users to easily demonstrate multiple personas that are different from their identities in the real world. Therefore, this study focuses on online multi-personas that establish multiple fake identities in the SNS environment. The main objective of this study is to investigate factors that affect online multi-personas. Fake online identities can have various negative consequences such as cyber bullying, cyber vandalism, or antisocial behavior. Since the boundary between the online and offline worlds is fading fast, these negative aspects of online behavior may influence offline behaviors as well. This study focuses on teenagers who often create multi-personas online. According to previous studies, personal identities are usually established during a person's youth. Based on data on 664 teenage users, this study identifies four emotional factors, namely, closeness with others, relative deprivation, peer pressure and social norms. According to data analysis results, three factors (except closeness with others) have positive correlations with users' multi-personas. This study contributes to the literature by identifying the factors that cause young people to form online multi-personas, an issue that has not been fully discussed in previous studies. From a practical perspective, this study provides a basis for a safe online environment by explaining the reasons for creating fake SNS identities.

• The Journal of Industrial Distribution & Business
• /
• v.11 no.5
• /
• pp.45-52
• /
• 2020

Purpose: In the 4th industrial revolution, services that incorporate various smart technologies in the tourism sector have begun to gain popularity. Accordingly, academic discussions on smart tourism have also started to become active in various fields. Despite recent research, the definition of smart tourism is still ambiguous, and it is not easy to differentiate its scope or characteristics from traditional tourism concepts. Thus, this study aims to analyze the perception of smart tourism exposed online to identify the current point of smart tourism in Korea and present the research direction for conceptualizing smart tourism suitable for the domestic situation. Research design, data, and methodology: This study analyzes the perception of smart tourism exposed online based on 20,198 news data from portal sites over the past six years. Data on words used with smart tourism were collected from the leading portal sites Naver, Daum, and Google. Text mining techniques were applied to identify the social awareness status of smart tourism. Network analysis was used to visualize the results between words related to smart tourism, and CONCOR analysis was conducted to derive clusters formed by words having similarity. Results: As a result of keyword analysis, the frequency of words related to the development and construction of smart tourism areas was high. The analysis of the centrality of the connection between words showed that the frequency of keywords was similar, and that the words "smartphones" and "China" had relatively high connection centrality. The results of network analysis and CONCOR indicated that words were formed into eight groups including related technologies, promotion, globalization, service introduction, innovation, regional society, activation, and utilization guide. The overall results of data analysis showed that the development of smart tourism cities was a noticeable issue. Conclusions: This study is meaningful in that it clearly reflects the differences in the perception of smart tourism between online and research trends despite various efforts to develop smart tourism in Korea. In addition, this study highlights the need to understand smart tourism concepts and enhance academic discussions. It is expected that such academic discussions will contribute to improving the competitiveness of smart tourism research in Korea.

• Informatization Policy
• /
• v.25 no.3
• /
• pp.52-74
• /
• 2018

In February 2018, the Korean government established the "Comprehensive Plans for Government Innovation" in order to realize 'the people-centered government'. The core of the comprehensive plans is participation of the people, which is very similar to open innovation where social issues are solved by ideas and capabilities of the private sector rather than those of the government. Therefore, this study was conducted by extracting open innovation topics through topic modeling based on LDA(Latent Dirichlet Allocation) as English abstract-data from 2003, when the plans for open innovation was first announced, to April 2018. Based on the extracted results, it also conducted a comparative analysis with "Comprehensive Plans for Government Innovation." The study has significant implications in that it derives the relationship between the subjects, analyzes the present policies of Korea on open innovation and suggests directions for development.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.11
• /
• pp.730-737
• /
• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.2
• /
• pp.85-101
• /
• 2021

This study aims to gain insight through understanding the Metaverse, which has recently become a hot topic. The study utilizes the methods of case study and News Bigdata Analysis Services. The Metaverse can be defined as a world with no separation between the virtual and real worlds. Currently, the Metaverse is dominated mainly by the MZ generation, but just like smartphones have quickly entered our lives, the Metaverse will soon, too, become a part of our lives. To follow up on this change, all companies, including global companies, are going after the Metaverse. Today, the Metaverse is successfully being used in all types of fields, including gaming, performing arts, business, etc., and its essential technologies include VR/AR/MR/XR and AI. This study intends to help understand the Metaverse through a case analysis of Zepeto, which has 200 million users worldwide. On Zepeto, users can decorate their own avatars, hang out with friends, go to art galleries and performances, and create and sell items. Of these users, 90% are from outside of South Korea, and 80% are teenagers. With most of the users being underage, many legal and social problems also follow. Nevertheless, who will be the first to conquer the new world of the Metaverse will continue to be a big issue. This study also analyzes domestic news articles about the Metaverse by utilizing the BigKinds system. Starting in 1996, the number of articles about the Metaverse each year remains single digit, until in 2020 when the number sharply rises to 86 news. As of June 2021, there are 1,663 articles on the Metaverse. This study suggests that the Metaverse should now be carefully examined and closely followed.