• Fashion & Textile Research Journal
• /
• v.24 no.5
• /
• pp.593-604
• /
• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.

• The Journal of Korean Medicine
• /
• v.42 no.4
• /
• pp.120-132
• /
• 2021

Objectives: The prevalence of Parkinson's disease is on an upward trend along with an increase in the aging population but there is no available treatment that halts the progression of neurodegeneration. This study reports a numerical analysis on Donguibogam and suggests novel herbal drugs, which have never been researched before but found to be deemed effective in this study. Methods: Referring to 71 Korean medicine symptom terms that represent the symptoms of Parkinson's disease, 4170 prescriptions described in Donguibogam were classified into two groups based on whether their main effects were effective for Parkinson's disease or not. Comparing the two groups, the chi-square test was performed to select statistically significant herbs, while the t-test, Wilcoxon test, and descriptive statistics were performed to determine the appropriate dose. Results: One hundred and twenty-seven prescriptions effective for Parkinson's disease were identified. The chi-square test determined 17 herbs that are effective for symptomatic treatment. Among the medicinal herbs, the authors suggest Osterici seu Notopterygii Radix et Rhizoma, Ephedrae Herba, Aconiti Tuber, Myrrha, Sinomeni Caulis et Rhizoma, and Aconiti Kusnezoffii Tuber as herbal candidates that have never been studied for Parkinson's disease. Through the statistical tests, it was judged that the mean value of the dose of the entire prescription was the appropriate dose for each herb. Conclusions: Seventeen herbs were selected for Parkinson's disease and the appropriate daily dose were calculated. Furthermore, this study presented a new process that applies a statistical method to traditional medical literature and preselecting herbs deemed effective for specific diseases.

• Archives of Plastic Surgery
• /
• v.50 no.1
• /
• pp.3-9
• /
• 2023

Background In breast reconstruction, synthetic meshes are frequently used to replace acellular dermal matrix (ADM), since ADM is expensive and often leads to complications. However, there is limited evidence that compares the types of substitutes. This study aimed to compare complications between materials via a network meta-analysis. Methods We systematically reviewed studies reporting any type of complication from 2010 to 2021. The primary outcomes were the proportion of infection, seroma, major complications, or contracture. We classified the intervention into four categories: ADM, absorbable mesh, nonabsorbable mesh, and nothing used. We then performed a network meta-analysis between these categories and estimated the odds ratio with random-effect models. Results Of 603 searched studies through the PubMed, MEDLINE, and Embase databases, following their review by two independent reviewers, 61 studies were included for full-text reading, of which 17 studies were finally included. There was a low risk of bias in the included studies, but only an indirect comparison between absorbable and non-absorbable mesh was possible. Infection was more frequent in ADM but not in the two synthetic mesh groups, namely the absorbable or nonabsorbable types, compared with the nonmesh group. The proportion of seroma in the synthetic mesh group was lower (odds ratio was 0.2 for the absorbable and 0.1 for the nonabsorbable mesh group) than in the ADM group. Proportions of major complications and contractures did not significantly differ between groups. Conclusion Compared with ADM, synthetic meshes have low infection and seroma rates. However, more studies concerning aesthetic outcomes and direct comparisons are needed.

• Journal of Information Technology Services
• /
• v.22 no.3
• /
• pp.141-165
• /
• 2023

With the advent of the fourth industrial revolution, technologies such as the Internet of Things, artificial intelligence and cloud computing are developing rapidly, and smart homes enabled by these technologies are rapidly gaining popularity. To gain a competitive advantage in the global market, companies must understand the differences in consumer needs in different countries and cultures and develop corresponding business strategies. Therefore, this study conducts a comparative analysis of consumer reviews of smart homes in South Korea and China. This study collected online reviews of SmartThings, ThinQ, Msmarthom, and MiHome, the four most commonly used smart home apps in Korea and China. The collected review data is divided into satisfied reviews and dissatisfied reviews according to the ratings, and topics are extracted for each review dataset using LDA topic modeling. Next, the extracted topics are classified according to five evaluation factors of Perceived Usefulness, Reachability, Interoperability,Trustness, and Product Brand proposed by previous studies. Then, by comparing the importance of each evaluation factor in the two datasets of satisfaction and dissatisfaction, we find out the factors that affect consumer satisfaction and dissatisfaction, and compare the differences between users in Korea and China. We found Trustness and Reachability are very important factors. Finally, through language network analysis, the relationship between dissatisfied factors is analyzed from a more microscopic level, and improvement plans are proposed to the companies according to the analysis results.

• International Journal of Computer Science & Network Security
• /
• v.23 no.3
• /
• pp.67-80
• /
• 2023

Rice is an important food crop for most of the population in Nowadays, psychologists consider social media an important tool to examine mental disorders. Among these disorders, depression is one of the most common yet least cured disease Since abundant of writers having extensive followers express their feelings on social media and depression is significantly increasing, thus, exploring the literary text shared on social media may provide multidimensional features of depressive behaviors: (1) Background: Several studies observed that depressive data contains certain language styles and self-expressing pronouns, but current study provides the evidence that posts appearing with self-expressing pronouns and depressive language styles contain high emotional temperatures. Therefore, the main objective of this study is to examine the literary cyber writers' posts for discovering the symptomatic signs of depression. For this purpose, our research emphases on extracting the data from writers' public social media pages, blogs, and communities; (3) Results: To examine the emotional temperatures and sentences usage between depressive and not depressive groups, we employed the SentiStrength algorithm as a psycholinguistic method, TF-IDF and N-Gram for ranked phrases extraction, and Latent Dirichlet Allocation for topic modelling of the extracted phrases. The results unearth the strong connection between depression and negative emotional temperatures in writer's posts. Moreover, we used Naïve Bayes, Support Vector Machines, Random Forest, and Decision Tree algorithms to validate the classification of depressive and not depressive in terms of sentences, phrases and topics. The results reveal that comparing with others, Support Vectors Machines algorithm validates the classification while attaining highest 79% f-score; (4) Conclusions: Experimental results show that the proposed system outperformed for detection of depression trends in literary cyber writers using sentiment analysis.

• Korean Journal of Otorhinolaryngology-Head and Neck Surgery
• /
• v.55 no.8
• /
• pp.498-507
• /
• 2012

Background and Objectives We aimed to develop a Korean version of the velopharyngeal insufficiency (VPI) speech corpus system. Subjects and Method After developing a 3-channel simultaneous speech recording device capable of recording nasal/oral and normal compound speech separately, voice data were collected from VPI patients aged more than 10 years with/without the history of operation or prior speech therapy. This was compared to a control group for which VPI was simulated by using a french-3 nelaton tube inserted via both nostril through nasopharynx and pulling the soft palate anteriorly in varying degrees. The study consisted of three transcriptors: a speech therapist transcribed the voice file into text, a second transcriptor graded speech intelligibility and severity and the third tagged the types and onset times of misarticulation. The database were composed of three main tables regarding (1) speaker's demographics, (2) condition of the recording system and (3) transcripts. All of these were interfaced with the Praat voice analysis program, which enables the user to extract exact transcribed phrases for analysis. Results In the simulated VPI group, the higher the severity of VPI, the higher the nasalance score was obtained. In addition, we could verify the vocal energy that characterizes hypernasality and compensation in nasal/oral and compound sounds spoken by VPI patients as opposed to that characgerizes the normal control group. Conclusion With the Korean version of VPI speech corpus system, patients' common difficulties and speech tendencies in articulation can be objectively evaluated. Comparing these data with those of the normal voice, mispronunciation and dysarticulation of patients with VPI can be corrected.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.243-251
• /
• 2024

As digital video communication technology advances and global interactions become more frequent, cultural barriers between countries are gradually diminishing. Subtitles in TV content reflect the writing systems and cultural contexts of different countries, aiding in the comprehension of program content. However, when comparing subtitles between countries with different writing systems, variations in format and the representation of onomatopoeic expressions become apparent. Therefore, this study focuses on analyzing the differences and peculiarities in the onomatopoeic subtitles of Korean and Chinese variety shows, which are based on distinct writing systems. Through this analysis, the study aims to understand how differences in writing systems influence the representation of onomatopoeic subtitles and viewer experience. This investigation is expected to provide creative inspiration for variety show producers and facilitate cross-cultural communication.

• Journal of Science Education
• /
• v.36 no.1
• /
• pp.139-152
• /
• 2012

The purpose of this study is to provide some suggestions for future improvement of scientific inquiry activities in Korean elementary science textbook. The modified framework of Lee(2005) and Millar et al.(1998) was used to compare inquiry activities in the Korean and Singaporean science textbooks. The results of this study are as follows: Korean text books have more activities than Singapore's, but both countries have similar time allotment for science classes. In the area of 'inquiry process skill', Singapore is more balanced in 'Basic inquiry process skills' and 'Integrated inquiry process skills' than Korea. Singapore's integrated inquiry rate is also higher than Korea's. Next the results of comparing leaning objectives to scientific inquiry activities shows that Korean text books tend to focus on 'contents objectives', while Singapore's text books focus on balancing 'contents objectives' and 'process objectives'. Korean science textbooks encourage students to communicate the results of experiments but in most case these communication activities are actually not performed. Lastly Korea and Singapore have low degree of openness in inquiry activities. Remarkably 'Suggest questions' are totally conducted by teachers. This study implies that Korean science textbooks should have lower amounts of inquiry activities to accomodate enough time for communication about results. Next we need to make balance not only 'Basic inquiry process skills' and 'Integrated inquiry process skills' but also 'Content objectives' and 'Process objectives'. Lastly we need to make student to be the leader in science classes through encouraging them to plan procedures for experiments and to discover results by themselves.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.175-197
• /
• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.143-156
• /
• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.