• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.129-139
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• 2023

Forecasting volatility is essential to avoiding the risk caused by the uncertainties of an financial asset. Complicated financial volatility features such as ambiguity between non-stationarity and stationarity, asymmetry, long-memory, sudden fairly large values like outliers bring great challenges to volatility forecasts. In order to address such complicated features implicity, we consider machine leaning models such as LSTM (1997) and GRU (2014), which are known to be suitable for existing time series forecasting. However, there are the problems of vanishing gradients, of enormous amount of computation, and of a huge memory. To solve these problems, a causal temporal convolutional network (TCN) model, an advanced form of 1D CNN, is also applied. It is confirmed that the overall forecasting power of TCN model is higher than that of the RNN models in forecasting VIX, VXD, and VXN, the daily volatility indices of S&P 500, DJIA, Nasdaq, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.137-144
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• 2016

The purpose of this study is to investigate the structural relationships among goal orientation, teaching presence, approaches to leaning, satisfaction and academic achievement. For this study, the web survey was administered to 235 learners who participated in distance lifelong education centers of A, B, and C university in South Korea. Structural equation modeling (SEM) analysis was conducted in order to examine the causal relationships among the variables. The results indicated that first, mastery-approach goal and teaching presence had positive effects on deep approach. Second, mastery-approach goal showed negative effects on surface approach, while teaching presence did not. Third, deep approach had positive effects on satisfaction, Fourth, surface approach had negative effects on satisfaction. Fifth, deep approach showed positive effects. Last, surface approach showed negative effects on academic achievement. Based on the result of the research, the study propose the constructive foundation for providing strategies raising the satisfaction and academic achievement in distance life-long education.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.61-66
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• 2018

인과관계 추출이란 어떠한 문장에서 인과관계가 존재하는지, 인과관계가 존재한다면 원인과 결과의 위치까지 분석하는 것을 말한다. 하지만 인과관계 관련 연구는 그 수가 적기 때문에 말뭉치의 수 또한 적으며, 기존의 말뭉치가 존재하더라도 인과관계의 특성상 새로운 도메인에 적용할 때마다 데이터를 다시 구축해야 하는 문제가 있다. 따라서 본 논문에서는 도메인 특화에 따른 데이터 구축비용 문제를 최소화하면서 새로운 도메인에서 인과관계 모델을 잘 구축할 수 있는 통계 기반 모델을 이용한 인과관계 데이터 확장 방법과 도메인에 특화되지 않은 일반적인 언어자질과 인과관계에 특화된 자질을 심층 학습 기반 모델에 적용함으로써 성능 향상을 보인다.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.505-512
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• 2021

Background: The occurrence of environmental disease is known to be associated with chronic exposure to toxic chemicals, including waterborne contaminants, air/indoor pollutants, asbestos, ingredients in humidifier disinfectants, etc. Objectives: In this study, we reviewed toxicological studies related to environmental disease as defined by the Environmental Health Act in Korea and toxic chemicals. We also suggested a direction for future toxicological research necessary for the prevention and management of environmental disease. Methods: Trends in previous studies related to environmental disease were investigated through PubMed and Web of Science. A detailed review was provided on toxicological studies related to the humidifier disinfectants. We identified adverse outcome pathways (AOPs) that can be linked to the induction of environmental diseases, and proposed a chemical screening system that uses AOP, chemical toxicity big data, and deep learning models to select chemicals that induce environmental disease. Results: Research on chemical toxicity is increasing every year, but there is a limitation to revealing a clear causal relationship between exposure to chemicals and the occurrence of environmental disease. It is necessary to develop various exposure- and effect-biomarkers related to disease occurrence and to conduct toxicokinetic studies. A novel chemical screening system that uses AOP and chemical toxicity big data could be useful for selecting chemicals that cause environmental diseases. Conclusions: From a toxicological point of view, developing AOP related to environmental diseases and a deep learning-based chemical screening system will contribute to the prevention of environmental diseases in advance.

• Information Systems Review
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• v.25 no.3
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• pp.59-81
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• 2023

Elderly suicide problem has become worse in South Korea. With a rapid aging of the population, the trend of suicide among the elderly is expected to accelerate, preventing elderly suicide has been considered an important societal problem. Thus, we aim to investigate various factors that explain suicidal ideation and to develop a predictive model for suicidal ideation in the context of elderly people in South Korea. To this end, this study contributes to addressing the elderly suicide problem. By using seven-year panel data from the Korea Welfare Panel Survey, we extract various potential causal factors for elderly suicidal ideation based on interpersonal theory of suicide and social disorganization theory. Then a panel logit model was employed to assess the impacts of potential factors on suicidal ideation and deep learning and machine learning algorithms were used to develop a predictive model for suicidal ideation of elderly people. The results of our study provide practical implications for preventing elderly suicide by identifying causal factors of suicidal ideation and a high suicidal risk group of the elderly. This study sheds light on synergy of mixed methodology and provides various academic implications.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.