• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.45-58
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• 2019

As modern people's daily lives are becoming more harsh in Korea, the so-called Kidults generation has appeared since a few years ago as adults have come back to their childhood sensibility and are exposed to various cultures online, and there are many people who have a hobby for collecting toys. However, as there is currently no formalized system for individual toy trade online, it is difficult to acquire expertise and share information with each other through a major portal site's $caf{\acute{e}}$, and is exposed to security or fraud while trading toys. In this paper, we design and implementation of a web-based toy trading system. Analyzing the advantages and disadvantages of the various trading and relay systems currently in use, it will provide opportunities for professional toy knowledge and information exchange to many users who have a hobby of collecting toys, and will greatly help vitalize the toy market through a secure and convenient trading environment between individuals.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.71-89
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• 2019

In order to come up with satisfying product and improvement, firms use traditional marketing research methods to obtain consumers' opinions and further try to reflect them. Recently, gathering data from consumer communication platforms like internet and SNS has become popular methods. Meanwhile, with the development of information technology, mobile companies are launching new digital products for children to protect them from harmful content and provide them with necessary functions and information. Among these digital products, Kids Phone, which is a wearable device with safe functions that enable parents to learn childern's location. Kids phone is relatively cheaper and simpler than smartphone but it is noted that there are several problems such as some useless functions and frequent breakdowns. This study analyzes the reviews of Kids phones from domestic mobile companies, identifies the characteristics, strengths and weaknesses of the products, proposes improvement methods strategies for devices and services through SNS consumer analysis. In order to do that customer review data from online shopping malls was gathered and was further analyzed through text mining methods such as TF/IDF, Sentiment Analysis, and network analysis. Customer review data was gathered through crawling Online shopping Mall and Naver Blog/$Caf\acute{e}$. Data analysis and visualization was done using 'R', 'Textom', and 'Python'. Such analysis allowed us to figure out main issues and recent trends regarding kids phones and to suggest possible service improvement strategies based on sentiment analysis.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.129-134
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• 2017

In the present society, our current technology associated with bakery industry has been improved as much as technical development can get abreast of the Western one where the bread has originated and has been awarded and ranked in the highest level of many bakery or pastry competitions. In these trends, many people are running for high value added business such as bakery industry and bakery $caf{\acute{e}}$, etc. with big interest. However, high labor cost of technician and difficulties in human resource management become obstruction factors in the growth of the bakery industries. Therefore, in this paper, the designed system for both dividing and rounding dough quickly and exactly at the same time was studied. The main function of this system is to divide the original dough into 3 tracks and then, to place 4 doughs in the inner track, 12 doughs in the mid track, 20 doughs in the outer track, totally 36 doughs in a routine. It takes much energy because 36 doughs can be completed in a routine. Therefore, this system uses hydraulic pressure and a 0.75Kw induction motor is used for dough rounding. This system can make primarily fermented dough into 36 divided doughs very quickly and exactly on a guide panel at the desired weight by dividing it within 1-9 seconds and by rounding each within 1-9 seconds. This system is very effective in bakery industry to minimize labor cost and it is expected to supply more hygienic products to the customers.

• Korean Journal of Health Education and Promotion
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• v.30 no.3
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• pp.59-73
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• 2013

Objectives: The study is aimed at examining the individual reasons and regional/environmental factors of online search on suicide using social big data to predict practical behaviors related to suicide and to develop an online suicide prevention system on the governmental level. Methods: The study was conducted using suicide-related social big data collected from online news sites, blogs, caf$\acute{e}$s, social network services and message boards between January 1 and December 31, 2011 (321,506 buzzes from users assumed as adults and 67,742 buzzes from those assumed as teenagers). Technical analysis and development of the suicide search prediction model were done using SPSS 20.0, and the structural model, nd multi-group analysis was made using AMOS 20.0. Also, HLM 7.0 was applied for the multilevel model analysis of the determinants of search on suicide by teenagers. Results: A summary of the results of multivariate analysis is as follows. First, search on suicide by adults appeared to increase on days when there were higher number of suicide incidents, higher number of search on drinking, higher divorce rate, lower birth rate and higher average humidity. Second, search on suicide by teenagers rose on days when there were higher number of teenage suicide incidents, higher number of search on stress or drinking and less fine dust particles. Third, the comparison of the results of the structural equation model analysis of search on suicide by adults and teenagers showed that teenagers were more likely to proceed from search on stress to search on sports, drinking and suicide, while adults significantly tended to move from search on drinking to search on suicide. Fourth, the result of the multilevel model analysis of determinants of search on suicide by teenagers showed that monthly teenagers suicide rate and average humidity had positive effect on the amount of search on suicide. Conclusions: The study shows that both adults and teenagers are influenced by various reasons to experience stress and search on suicide on the Internet. Therefore, we need to develop diverse school-level programs that can help relieve teenagers of stress and workplace-level programs to get rid of the work-related stress of adults.

• The Journal of Bigdata
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• v.1 no.1
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• pp.67-75
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• 2016

The study analyzed big data extracted from Google and social media to identify factors related to searches on cyber bullying in Korea and America. Korea's cyber bullying analysis was conducted social big data collected from online news sites, blogs, $caf{\acute{e}}s$, social network services and message for between January 1, 2011 and March 31, 2013. Google search trends for the search words of stress, exercise, drinking, and cyber bullying were obtained for January 1, 2004 and December 22, 2013. The main results of this study were as follows: first, the significant factors stress were cyber bullying that Korea more than America. Secondly, a positive relationship was found between stress and drinking, exercise and cyber bullying both Korea and America. Thirdly, significant differences were found all path both Korea and America. The study shows that both adults and teenagers are influenced in Korea. We need to develop online application that if cyber bullying behavior was predicted can intervene in real time because these actual cyber bullying-related exposure to psychological and behavioral characteristic.

• Tuberculosis and Respiratory Diseases
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• v.50 no.1
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• pp.122-126
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• 2001

A non-traumatic, spontaneous hemothorax is rare. The most common causes are coagulopathy, due to anticoagulation treatment, and cancers with a metastasis to the pleural surface. Other unusual causes include thoracic endometriosis, ruptured aortic aneurysm, pulmonary arterio-venous malformation, coagulopathy, Osler-Rendeu-Weber syndrome, Ehlers-Danlos syndrome et cetera. A type 1 neurofibromatosis(Von Recklinghausen's disease) is an autosomal dominant disease that is characterized by multiple skin tumors(neurofibroma) and abnormal skin pigmentation(caf$\acute{e}$-au-lait spots). Some are accompanied by vasculopathy, and are present with a spontaneous hemothorax. Such cases are unusual but fatal. We have recently experienced a case where a young male patient with neurofibromatosis initially presented with hypovolemic shock due to a spontaneous hemothorax. Later, aortography revealed that the cause of the hemothorax was a rupture of a pseudoaneurysm of the right internal mammary artery and as a result, an embolization was performed. Here we report this case with a review of the appropriate literature.

• Journal of the HCI Society of Korea
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• v.12 no.3
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• pp.25-33
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• 2017

Current video service market doesn't satisfy the users' needs who want to find new and interesting contents despite the vast amount of contents. Now it is continuously necessary to Study on technology and using experience is continuously required in online video service area to stimulate the watching motivation efficiently with such as recommendation or promotion. One of efficient ways of increasing the using motivation is to give the users pleasure when they use the services. This study focused on 'unexpected funny finding' as a strategy of providing pleasure of using. It was believed that it could increase the pleasure of using the service, if serendipity, which means unexpected pleasure, accidental finding such as finding a beautiful $caf{\acute{e}}$ or meeting a friend at a certain place unexpectedly, is applied. This study defines the serendipity as 'contents that give unexpected pleasure' at the online video environment. First it theoretically extracted the various characteristics of serendipity through reading many books. Next it verified the other concept of serendipity through the diary of users' survey to additionally extract the characteristics of serendipity at video environment that are hard to find in books. It formed estimation items for the characteristics of the extracted serendipity and tested them in youtube to confirm the characteristics of serendipity being found in video service and observe potential factors that make it. As a result if verified and confirmed four factors that cause serendipity at video environment. This study could be used as basic data to understand the concept of serendipity. It has an academic meaning in the point that it could be a useful reference for the future study that analyzes the role or effect of serendipity at IT area including online video service.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.