• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.289-300
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• 2021

SNS data was collected and analyzed by topic modeling techniques to examine recent trends in information education. By deriving keywords and topics for SW education and AI education, we not only attempted to discover insights ahead of the next revised curriculum but also suggested directions. According to the SNS data analysis, the contents of human resource development for software and the instructional method in schools are indicated as a high requirement. Meanwhile, SW education should be conducted through a separate curriculum from elementary school, and this was consistent with the opinion that it is necessary to be organized as a required subject. There was an opinion to support the schools since AI education is newly introduced in next revised national curriculum. The trends in SW education and AI education which are observed through SNS data analysis could be concluded to conduct the substantial operation of information education and curriculum organization.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1581-1587
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• 2022

Recently, deep learning has become more important in remote sensing data processing. Huge amounts of data for artificial intelligence (AI) has been designed and built to develop new technologies for remote sensing, and AI models have been learned by the AI training dataset. Artificial intelligence models have developed rapidly, and model accuracy is increasing accordingly. However, there are variations in the model accuracy depending on the person who trains the AI model. Eventually, experts who can train AI models well are required more and more. Moreover, the deep learning technique enables us to automate methods for remote sensing applications. Methods having the performance of less than about 60% in the past are now over 90% and entering about 100%. In this special issue, thirteen papers on how deep learning techniques are used for remote sensing applications will be introduced.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.629-632
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• 2022

Education for sustainable development is an education that helps learners of all ages acquire the knowledge, skills, and attitudes necessary to solve interconnected international challenges such as climate change and environmental problems.It is an integral component of the Sustainable Development Goals (SDGs) #4 and contributes to the 17 SDGs. In order to find out the trend of ESD, 2718 news data from January 1, 2000 to December 31, 2021 were collected through 26 media outlets.As key keywords, international organizations leading sustainable development education such as the UN and UNESCO, local governments including Dobong-gu, and major issues such as climate change and ecological change could be identified. This can be used as basic data for various studies as it can explore trends for ESD.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.69-90
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• 2022

As basic data that can systematically support and evaluate R&D activities as well as set current and future research directions by grasping specific trends in domestic academic research, I sought efficient ways to assign standardized subject categories (control keywords) to individual journal papers. To this end, I conducted various experiments on major factors affecting the performance of automatic classification, focusing on feature selection techniques, for the purpose of automatically allocating the classification categories on the National Research Foundation of Korea's Academic Research Classification Scheme to domestic journal papers. As a result, the automatic classification of domestic journal papers, which are imbalanced datasets of the real environment, showed that a fairly good level of performance can be expected using more simple classifiers, feature selection techniques, and relatively small training sets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.151-154
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• 2021

Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study was analyzed for 21 papers in the March 2021 among the journals of the Korea Institute of Information and Communication Engineering. In the final analysis results, the most frequently mentioned keyword was "Data", which ranked first 305 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.326-327
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• 2021

Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study were analyzed for 29 papers in a specific journal. In the final analysis results, the most frequently mentioned keyword was "Research", which ranked first 743 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.201-210
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• 2023

The purpose of this study is to analyze the general characteristics, effect size, and qualitative indicators of self-advocacy studies of people with developmental disabilities published in domestic academic journals and theses. For this purpose, among a total of 2153 papers related to self-advocacy published from 2000 to 2023, 41 studies with developmental disabilities as the keyword were selected, and the specific research results are as follows. Based on the results of this study, when developing a language intervention program related to self-advocacy for people with developmental disabilities, it is recommended to develop an intervention program based on the number of sessions of 10-19 in a learning situation with 20-30 people in adolescents and adults, or during the transition period. There are many studies limited to educational aspects such as special education and integrated education, and by applying this, it is hoped that a self-advocacy language intervention program will be developed at the level of language rehabilitation that can effectively and sophisticatedly assert self-assertion and self-rights after experiencing difficulties in communication.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.801-807
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• 2023

This study aims to examine the cases of overseas experience programs centered around middle school students in South Korea and to derive implications for future overseas experience programs. To achieve this, data were systematically collected through search engines based on keywords, followed by comparative analysis. Frequency analysis, independent sample t-tests, and cross-analysis were conducted using SPSS 23. The research findings are as follows: First, the programs are operated nationwide, with a focus on smaller schools in various regions, and are particularly active in the Jeolla provinces. Diverse public funding, such as from the board of education and local governments, has been invested, categorizing operational costs into full financial coverage among others. The programs primarily took place in Southeast Asian countries close to South Korea. Second, the purposes of these middle school overseas experience programs largely encompass career exploration, cultural experiences, tourism, and sister school visits. We hope that school-based overseas career exploration programs are actively operated to provide opportunities for enhancing global competence and global citizenship, as well as exploring career paths.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.82-88
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• 2024

As the modern maritime industry rapidly progresses through technological advancements, data processing technology is emphasized as a key driver of this development. Natural language processing is a technology that enables machines to understand and process human language. Through this methodology, we aim to develop a model that predicts the proportions of outcomes when entering new written judgments by analyzing the rulings of the Marine Safety Tribunal and learning the cause-providing ratios of previously adjudicated ship collisions. The model calculated the cause-providing ratios of the accident using the navigation applied at the time of the accident and the weight of key keywords that affect the cause-providing ratios. Through this, the accuracy of the developed model could be analyzed, the practical applicability of the model could be reviewed, and it could be used to prevent the recurrence of collisions and resolve disputes between parties involved in marine accidents.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.393-402
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• 2016

To compensate for limited the satisfaction survey currently conducted by Korea Metrological Administration (KMA), a sentiment analysis via a social networking service (SNS) can be utilized. From 2011 to 2014, with the sentiment analysis, Twitter who had commented 'KMA' had collected, then, using $Na{\ddot{i}}ve$ Bayes classification, we were classified into three sentiments: positive, negative, and neutral sentiments. An additional dictionary was made with morphemes appeared only in the positive, negative, and neutral sentiments of basic $Na{\ddot{i}}ve$ Bayes classification, thus the accuracy of sentiment analysis was improved. As a result, when sentiments were classified with a basic $Na{\ddot{i}}ve$ Bayes classification, the training data were reproduced about 75% accuracy rate. Whereas, when classifying with the additional dictionary, it showed 97% accuracy rate. When using the additional dictionary, sentiments of verification data was classified with about 75% accuracy rate. Lower classification accuracy rate would be improved by not only a qualified dictionary that has increased amount of training data, including diverse keywords related to weather, but continuous update of the dictionary. Meanwhile, contrary to the sentiment analysis based on dictionary definition of individual vocabulary, if sentiments are classified into meaning of sentence, increased rate of negative sentiment and change in satisfaction could be explained. Therefore, the sentiment analysis via SNS would be considered as useful tool for complementing surveys in the future.