• Journal of Dental Anesthesia and Pain Medicine
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• v.22 no.2
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• pp.87-96
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• 2022

This study aimed to assess the combined use of extraoral vibratory stimulation and extraoral cooling in reducing the pain (subjective and objective) of dental local anesthesia administration in children. PubMed, Cochrane Central Register of Controlled Trials, and Ovid SP databases were searched up to July 2021. Article titles were screened and full-text evaluations of the selected articles were performed. Finally, seven studies (391 children, aged 4 - 12 years) were included in this qualitative and quantitative analysis. The pooled data determined the combined effect of extraoral vibration and extraoral cooling as a single measure. Extraoral vibration or cooling alone were not compared. The measured primary and secondary outcomes were pain perception and subjective and objective pain, respectively. When compared with the control, extraoral vibration and cooling resulted in significant differences in the mean combined data for the variables, pain perception, and pain reaction. Children's subjective pain as measured by pain scores were reduced when extraoral vibration and cooling was used during local anesthesia administration (mean difference -3.52; 95% confidence interval [-5.06 - 1.98]) and objective pain (mean difference -1.46; 95% confidence interval [-2.95 - 0.02] ; mean difference -1.93; 95% confidence interval [-3.72 - 0.14]). Within the confines of this systematic review, there is low-quality evidence to support the use of combined extraoral vibration and cooling for reducing pain (subjective and objective) during intraoral local anesthesia administration in children.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• Journal of Creative Information Culture
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• v.6 no.2
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• pp.57-64
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• 2020

As the information society gradually advances, various opinions overflow and their complexity increases. As the results, it was made more difficult to derive important issues and properly respond to those problems. Accordingly, it is necessary to get a handle on emerging problems in education in addition to existing discourses and issues. This study aimed at examining the issues of education by analyzing the petitions posted under 'parenting and education' category on National Petition board. In order to offer objective and detailed results, we employed the topic modeling based LDA algorithm, which is an effective method to extract topics in multiple documents. Nine topics were derived as the result of the analysis and the relationship among those topics was visualized. The values of this study exist in that the derived topics represent important issues that reflect the public opinions.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.291-301
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• 2021

The purposes of this paper were to provide the basic segments of qualitative research to assist the understanding of qualitative research and to provide implications of indicators (i.e., criteria) to determine the rigor of the qualitative research in the area of Adapted Physical Education (APE). This paper was divided into five sub-categories to facilitate understanding the qualitative research, which were (a) the epistemological stances, (b) the data collection methods (c) the data analysis (d) the trustworthiness and (e) the implications of Adapted Physical Activity Taxonomy (APAT) in APE. Qualitative researchers deliver their understanding of human experience and knowledge by means of text rather than number so their explanations and understanding tend be subjective and distinct from otherwise interpretaotherwise (Crotty, 1998; Pitney& Parker, 2009). Examining the rigor of qualitative research is therefore concerned due to the complexity of understanding of reality (Goodwin 2020; Zitomer & Goodwin, 2014). In the meantime the roles of qualitative research have been highlighted in educational research including the area of APE because qualitative research enables researchers to examine different voices of school members (e.g., students with and without disabilities parents teachers Goodwin, 2020; Hodge et al., 2019; Zitomer & Goodwin, 2014).

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.427-436
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• 2021

The purpose of this study is to implement a machine learning language model that learns literary texts. Literary texts have an important characteristic that pairs of question-and-answer are not frequently clearly distinguished. Also, literary texts consist of pronouns, figurative expressions, soliloquies, etc. They hinder the necessity of machine learning using literary texts by making it difficult to learn algorithms. Algorithms that learn literary texts can show more human-friendly interactions than algorithms that learn general sentences. For this goal, this paper proposes three text correction tasks that must be preceded in researches using literary texts for machine learning language model: pronoun processing, dialogue pair expansion, and data amplification. Learning data for artificial intelligence should have clear meanings to facilitate machine learning and to ensure high effectiveness. The introduction of special genres of texts such as literature into natural language processing research is expected not only to expand the learning area of machine learning, but to show a new language learning method.

• Journal of People, Plants, and Environment
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• v.24 no.5
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• pp.485-498
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• 2021

Background and objective: This study was conducted to find out the status of plant utilization in the current textbooks by analyzing the plants by grade and subject in the national textbooks for all elementary school grades in the 2015 revised curriculum in Korea. Methods: The data collected was analyzed using Microsoft Office Excel to obtain the frequency and ratio of collected plant data and SPSS for Windows 26.0 to determine learning content areas by grade and the R program was used to visualize the learning content areas. Results: A total of 232 species of plants were presented 1,047 times in the national textbooks. Based on an analysis of the plants presented by grade, the species that continued to increase in the lower grades tended to decrease in the fifth and sixth grades, the upper grades of elementary school. As for the number and frequency of plant species by subject, Korean Language had the highest number and frequency of plant species. The types of presentation of plants in textbooks were mainly text, followed by illustrations and photos of plants, which were largely used in first grade textbooks. In addition, as for the area of learning contents in which plants are used, in the lower grades, plants were used in the linguistic domain, and in the upper grades, in the botanical and environmental domains of the natural sciences. Herbaceous plants were presented more than woody plants, and according to an analysis of the plants based on the classification of crops, horticultural crops were presented the most, followed by food crops. Out of horticultural crops, flowering plants were found the most diversity with 63 species, but the plants that appeared most frequently were fruit trees that are commonly encountered in real life. Conclusion: As a result of this study, various plant species were included in elementary school textbooks, but most of them were horticultural crops encountered in real life depending on their use. Nevertheless, plant species with high frequency have continued a similar trend of frequency from the previous curriculums. Therefore, in the next curriculum, plant learning materials should be reflected according to social changes and students' preference for plants.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.261-266
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• 2022

The full-fledged digital age derived from the fourth industrial revolution and the impact of COVID-19 lead to changes in various fields, including companies. In other words, the importance of cloud computing is being emphasized in the rapidly changing digital environment due to the rapid growth of the cloud market due to the rapid increase in digital services. The cloud may be one of the representative strategies for sustainable growth and survival in various fields as well as related industries. Although there have been a variety of studies on the cloud, the tendency of them has been not been adequately examined. This paper, therefore, analyzed the tendency of studies on the cloud computing. by using SCOPUS, the database of overseas academic journals using both R and LAD technique. The findings showed that many studies with high interest in the cloud computing have been conducted, the cloud computing were most often drawn from an analysis on key words. Moreover, various key words, including cloud, cloud and computing, data and computing were drawn, except for the theme of cloud computing. It is expected that could be used as a basic data, in that they provide the foundation for activating the related industries in terms of practice of the cloud computing.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.65-79
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• 2022

This study identified smart manufacturing technologies using patent and topic modeling, and compared the technology development trends in countries such as the United States, Japan, Germany, China, and South Korea. To this purpose, this study collected patents in the United States and Europe between 1991 and 2020, processed patent abstracts, and identified topics by applying latent Dirichlet allocation model to the data. As a result, technologies related to smart manufacturing are divided into seven categories. At a global level, it was found that the proportion of patents in 'data processing system' and 'thermal/fluid management' technologies is increasing. Considering the fact that South Korea has relative competitiveness in thermal/fluid management technologies related to smart manufacturing, it would be a successful strategy for South Korea to promote smart manufacturing in heavy and chemical industry. This study is significant in that it overcomes the limitations of quantitative technology level evaluation proposed a new methodology that applies text mining.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.57-66
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• 2022

In this paper, we propose a legal document search method that uses the Sentence-BERT model. The general public who wants to use the legal search service has difficulty searching for relevant precedents due to a lack of understanding of legal terms and structures. In addition, the existing keyword and text mining-based legal search methods have their limits in yielding quality search results for two reasons: they lack information on the context of the judgment, and they fail to discern homonyms and polysemies. As a result, the accuracy of the legal document search results is often unsatisfactory or skeptical. To this end, This paper aims to improve the efficacy of the general public's legal search in the Supreme Court precedent and Legal Aid Counseling case database. The Sentence-BERT model embeds contextual information on precedents and counseling data, which better preserves the integrity of relevant meaning in phrases or sentences. Our initial research has shown that the Sentence-BERT search method yields higher accuracy than the Doc2Vec or TF-IDF search methods.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.787-793
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• 2022

After COVID-19, with the emergence of social distancing, non-face-to-face services, and home economics, visiting dining out is rapidly being replaced by non-face-to-face dining out. The purpose of this study is to find ways to create a safe dining culture centered on living quarantine in line with the changing trend of the restaurant industry after the outbreak of COVID-19, establish the direction of food culture improvement projects, and enhance the effectiveness of the project. This study used TEXTOM to collect and refine search frequency, perform TF-IDF analysis, and Ucinet6 programs to implement visualization using NetDraw from January 1, 2018 to October 31, 2019 and December 31, 2021, and identified the network between nodes of key keywords. Finally, clustering between them was performed through Concor analysis. As a result of the study, if you check the frequency of searches before and after COVID-19, it can be seen that the COVID-19 pandemic greatly affects the changes in the restaurant industry.