• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Economic and Environmental Geology
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• v.57 no.2
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• pp.119-128
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• 2024

In Australia & Canada, architectural rock and aggregate are one of the mineral resources, and related data and information provided integrated with them. In these countries, the provided data and information, through the information system of local government and national geological survey organizations, are interactive maps, geological and thematic maps, exploration data set, 3 dimension geological models, minning rights status, survey reports and related papers etc. However, in case of Korea, aggregate and architectural rock are not assigned as the kind of mineral resources in accordance to domestic mining law, and related geological data and information are not provided from comprehensive mineral information system established in public geoscience organizations. And the administrative and informative management are conducted separately through the different governmental organizations such as Ministry of construction, Korea forest service, geoscience institute & Korea Mine & Reclamation Corporation. For securing the supply of architectural rock and aggregate resources, and for the convenience of their development & utilization, the unified information system and governance reform for the related industry is needed.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.9-15
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• 2019

Artiodactyla, which is an even-toed mammal, widely inhabits worldwide. In recent years, wild Artiodactyla species have attracted public attention due to the rapid increase of crop damage and road-kill caused by wild Artiodactyla such as water deer and wild boar and the decrease of some species such as long-tailed goral and musk deer. In spite of such public attention, however, there have been few studies on Artiodactyla in Korea, and no studies have focused on the trend analysis of Artiodactyla, making it difficult to understand actual problems. Many recent studies on trend used text-mining and co-occurrence analysis to increase objectivity in the classification of research subjects by extracting keywords appearing in literature and quantifying relevance between words. In this study, we analyzed texts from research articles of three countries (Korea, China, and Japan) through text-mining and co-occurrence analysis and compared the research subjects in each country. We extracted 199 words from 665 articles related to Artiodactyla of three countries through text-mining. Three word-clusters were formed as a result of co-occurrence analysis on extracted words. We determined that cluster1 was related to "habitat condition and ecology", cluster2 was related to "disease" and cluster3 was related to "conservation genetics and molecular ecology". The results of comparing the rates of occurrence of each word clusters in each country showed that they were relatively even in China and Japan whereas Korea had a prevailing rate (69%) of cluster2 related to "disease". In the regression analysis on the number of words per year in each cluster, the number of words in both China and Japan increased evenly by year in each cluster while the rate of increase of cluster2 was five times more than the other clusters in Korea. The results indicate that Korean researches on Artiodactyla tended to focus on diseases more than those in China and Japan, and few researchers considered other subjects including habitat characteristics, behavior and molecular ecology. In order to control the damage caused by Artiodactyla and to establish a reasonable policy for the protection of endangered species, it is necessary to accumulate basic ecological data by conducting researches on wild Artiodactyla more.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.70-78
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• 2018

Recently, the conservation of mountainous landscape and compensation for diverse demands for mountain areas such as leisure, recreation and welfare are under discussion. The purpose of this study is to investigation the perception of mountainous landscapes by those who view and recognize the landscapes and to estimate economic value by estimating the willingness to pay for the management of mountainous landscapes. This study will provide data for the management of mountainous landscapes. As a result of comparing the perception between the territorial landscape and the mountain landscape, the mountain scenery was 3.96, the management level satisfaction was 3.28, and the management necessity was 4.38, which was higher than the national landscape, while the national landscape was satisfactory but the management level was insufficient. Jeju Island (39.0%) and Gangwon (38.6%) were chosen as the most scenic areas with beautiful forest and mountainous landscape resources. The aesthetic characteristics of the vast skyline of mountain scenery, the background of the area, and the mountainous landscape that forms the landmark were evaluated highly. And, it is considered that consciousness of mountainous landscape management is heightened by 86.8% of respondents, who positively answered the Mountainous Landscape Visual Impact Assessment before the development project. The per capita payment amount for mountainous landscape management was calculated to be 3,742 won and, based on the number of visitors to the mountain National Parks in 2016, it is estimated to have an economic value of about 169.5 billion won. Policymakers have limitations in the mountainous landscape management policies of the administrative subject. Establishing a consensus on the importance and necessity of landscape management by diagnosing the status of public perception is expected to help create more effective policy direction and implement strategies for the management of these areas.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.15-24
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• 2015

$CH_4$ is a trace gas and one of the key greenhouse gases, which requires continuous and systematic monitoring. The application of eddy covariance technique for $CH_4$ flux measurement requires a fast-response, laser-based spectroscopy. The eddy covariance measurements have been used to monitor $CO_2$ fluxes and their data processing procedures have been standardized and well documented. However, such processes for $CH_4$ fluxes are still lacking. In this note, we report the first measurement of $CH_4$ flux in a rice paddy by employing the eddy covariance technique with a recently commercialized wavelength modulation spectroscopy. $CH_4$ fluxes were measured for five consecutive days before and after the rice transplanting at the Gimje flux monitoring site in 2012. The commercially available $EddyPro^{TM}$ program was used to process these data, following the KoFlux protocol for data-processing. In this process, we quantified and documented the effects of three key corrections: (1) frequency response correction, (2) air density correction, and (3) spectroscopic correction. The effects of these corrections were different between daytime and nighttime, and their magnitudes were greater with larger $CH_4$ fluxes. Overall, the magnitude of $CH_4$ flux increased on average by 20-25% after the corrections. The National Center for AgroMeteorology (www.ncam.kr) will soon release an updated KoFlux program to public users, which includes the spectroscopic correction and the gap-filling of $CH_4$ flux.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.5
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• pp.42-52
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• 2009

Recreational forests are catching on as places for personal development through recreation, association with people, education about nature, mind relaxation and spiritual peace. However, the value and significance of recreational forests with various functions are easily overlooked. Whoever pays fees for admission to facilities are able to enjoy fresh air, a comfortable environment, and space for rest. It should be taken into consideration whether the fee which customers pay is appropriate for the value of nature they are enjoying. This study is involved in giving the right recognition to the value of recreation and environment by estimating economically the value of the environment in which visitors stay, and presenting the appropriate price. The most efficient way to achieve this goal is through an economic approach, which suggests following established research skills and yielding suitable and accurate amounts of money. The environmental value of a recreational forests is estimated through contingent valuation method(CVM), which is chosen among several methods to estimate public facilities because the value of recreational forests has strong characteristics as public facilities which are not traded in the market. The annual recreation value per person of surveyed recreational forests is Willingness To Pay(WTP) with a mean between about 16,000 won and 25,400 won. The recreation value of one recreational forest surveyed is annually between approximately 1.7 billions won and 2.7 billions won. The annual recreation value of 85 national and public recreational forests is presumed to be between about 140 billions won and 230 billions won. The presumed amount of money is the environment in which visitors can enjoy whenever they invest some money and time. Indeed, it is more than that; it provides visitors with a greater sense of satisfaction and the recognition of the preciousness of nature and the environment.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.415-426
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• 2021

Purpose: The purpose of this study is to create the deveopment plan for promotion of advanced disaster-safety awareness, which is noted as a major factor in the large disaster. Method: This study is to conduct theoretical review with regard to disaster management and safety awareness. Consciousness surveys on safety awareness and previous disaster case was analyzed to derive the cause of the disaster, and the development plan for promotion of advanced disaster-safety awareness was suggested. Result: In the survey on the public's sense of safety on the disaster management evaluation, 'Response' stage was well performed, but the 'Recovery' stage was not. Especially, it was found that disaster safety education at the 'Prevention' stage was very lacking. In the survey on the public's safety awareness, the awareness level of the evacuation facility was very low, information on infectious diseases and collapse accident was insufficient. Especially, it has been found that the awareness on safety regulation in daily life is very insufficient. Through the case study on previous disaster(COVID-19, Fire in Miryang Sejong Hospital, Forest fire in the east coas at 2004'), it was derived that the lack of safety awareness(such as safety insensitivity) was the main factor of the expansion of the damage scale. Conclusion: The development plan for promotion of advanced disaster-safety awareness are as follow. First, it is necessary to spread the safety culture movement through the expansion of safety education and safety promotion. Second, disaster confrontation training for the public should be implemented to improve the effectiveness of disaster response. Finally, it is necessary to change the individual awareness on safety. When these factors are implemented systematically, advanced disaster-safety awareness can be promoted. Ultimately, disaster accidents in our society can be reduced.

• Journal of Legislation Research
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• no.44
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• pp.211-239
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• 2013

In 2012, the Korean government has hosted the AFoCO Secretariat in Seoul. The AFoCO Secretariat is established by Agreement between the Governments of the Member States of the Association of Southeast Asian Nations and the Republic of Korea on Forest Cooperation (AFoCO Agreement) which is initiated by the Korea. The Korea government, however, does not have any laws and regulations to regulate the matter of legal status and legal personality of nationally hosted international organizations including the AFoCO Secretariat. Therefore, the legal status and legal personality of AFoCO Secretariat in international and domestic arena are still not clear. To articulate such issues and to propose some answers, this article analyzes the international and domestic legal theory and practice about the status and legal personality of public international organizations. As a result, it is common in the literature to delimit international organizations by some standards. One characteristic is that international organizations are usually created between states. A second characteristic is that they are established by means of a treaty. And as a third characteristic, international organizations must possess at least one organ which has a will distinct from the will of its members. According to those criteria, the AFoCO Secretariat can be categorized as a public international organization. It means that the AFoCO enjoys certain privileges and immunities as a public international organization and must confer legal capacity in Korea even there is no domestic laws and regulations conferred the status and legal personality to it. It, however, will be a better way to confer domestic legal personality on the AFoCO Secretariat through a domestic act like an "Act on the Assistance of International Organization Attraction". This act will stipulate the legal status of international organization in Korea including the privileges and immunities as well as the matter of assistance of hosting international organizations.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.