• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.197-217
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• 2018

Music is one of the most creative act that can express human sentiment with sound. Also, since music invoke people's sentiment to get empathized with it easily, it can either encourage or discourage people's sentiment with music what they are listening. Thus, sentiment is the primary factor when it comes to searching or recommending music to people. Regard to the music recommendation system, there are still lack of recommendation systems that are based on customer sentiment. An algorithm's that were used in previous music recommendation systems are mostly user based, for example, user's play history and playlists etc. Based on play history or playlists between multiple users, distance between music were calculated refer to basic information such as genre, singer, beat etc. It can filter out similar music to the users as a recommendation system. However those methodology have limitations like filter bubble. For example, if user listen to rock music only, it would be hard to get hip-hop or R&B music which have similar sentiment as a recommendation. In this study, we have focused on sentiment of music itself, and finally developed methodology of defining new index for music recommendation system. Concretely, we are proposing "SWEMS" index and using this index, we also extracted "Sentiment Pattern" for each music which was used for this research. Using this "SWEMS" index and "Sentiment Pattern", we expect that it can be used for a variety of purposes not only the music recommendation system but also as an algorithm which used for buildup predicting model etc. In this study, we had to develop the music recommendation system based on emotional adjectives which people generally feel when they listening to music. For that reason, it was necessary to collect a large amount of emotional adjectives as we can. Emotional adjectives were collected via previous study which is related to them. Also more emotional adjectives has collected via social metrics and qualitative interview. Finally, we could collect 134 individual adjectives. Through several steps, the collected adjectives were selected as the final 60 adjectives. Based on the final adjectives, music survey has taken as each item to evaluated the sentiment of a song. Surveys were taken by expert panels who like to listen to music. During the survey, all survey questions were based on emotional adjectives, no other information were collected. The music which evaluated from the previous step is divided into popular and unpopular songs, and the most relevant variables were derived from the popularity of music. The derived variables were reclassified through factor analysis and assigned a weight to the adjectives which belongs to the factor. We define the extracted factors as "SWEMS" index, which describes sentiment score of music in numeric value. In this study, we attempted to apply Case Based Reasoning method to implement an algorithm. Compare to other methodology, we used Case Based Reasoning because it shows similar problem solving method as what human do. Using "SWEMS" index of each music, an algorithm will be implemented based on the Euclidean distance to recommend a song similar to the emotion value which given by the factor for each music. Also, using "SWEMS" index, we can also draw "Sentiment Pattern" for each song. In this study, we found that the song which gives a similar emotion shows similar "Sentiment Pattern" each other. Through "Sentiment Pattern", we could also suggest a new group of music, which is different from the previous format of genre. This research would help people to quantify qualitative data. Also the algorithms can be used to quantify the content itself, which would help users to search the similar content more quickly.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.51-60
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• 2011

Authentication model in the wireless environment has many security vulnerabilities. However, there is no adapting standard method in this field. Therefore, we propose a fuzzy logic based authentication model to enhance the security level in the authentication environment. We use fuzzy logic based classification to construct our model, and also additionally utilize improved AHP and case-based reasoning for an appropriate decision making. We compute the context information by using the improved AHP method, use the proposed model to compute the security level for the input data, and securely apply the proposed model to the wireless environment which has diverse context information. We look forward to better security model including cloud computing by extending the proposed method in the future.

• Journal of The Korean Association For Science Education
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• v.33 no.2
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• pp.284-309
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• 2013

This study aims to explore how to characterize high school science inquiry. For this research, data were collected from fifteen science classes (18 hours), through observation and videotaping, interviews with a few students and their teacher, and documents such as lesson plan or activity sheet in 13 Science Core High Schools. All the data were transcribed and analyzed. Analyses of these transcripts were proceeded in three steps: first, classroom cases showing active interactions between teacher-students and among students were selected; second, according to cognitive process of inquiry (Chinn & Malhotra, 2002), each segment was analyzed and interpreted; lastly, distinctive cases were determined to show essential features of school science inquiry. Based on the analyses, we characterize high school science inquiry in terms of features of variables controlling-device improvement, design studies, evidence-explanation transformation, and reasoning to formulate explanations from evidence. Teachers' role and educational support were discussed as well as the practical characters or features of school science inquiry.

• Journal of The Korean Association of Information Education
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• v.5 no.2
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• pp.279-287
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• 2001

In this paper, we have defined and designed the structure of standardized XML content for supplying efficient e-Learning contents. We have also implemented the prototype of XML contents generator to create the educational contents easily. In addition, we have suggested the contents searching method using Case Base Reasoning and Bayesian belief network to supply XML contents suitable to learners request. The existing e-Learning system based on HTML could not customize and standardize, but XML contents can be reused and made an intelligent learning by supplying an adaptive content according to learners level. For evaluating the efficiency of designed XML content, we make the standard XML content for learning JAVA program in e-Learning system as well as discussing about the integrity and expanding the educational content. Finally, we have shown the architecture and effectiveness of the knowledge-based XML contents retrieval manager.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4191-4197
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• 2014

Value co-creation is an important business strategy these days in both the business-to-business (B2B) and business-to-consumer (B2C) markets. The aim of this study was to identify specialized resources and competences for value co-creation in the relationship network within a high-tech B2B market. A case of Taiwan Semiconductor Manufacturing Company Limited (TSMC) with customers and partners was chosen as the study case. Based on the observations, contents analysis of the secondary data and unstructured interviews with former TSMC employees, 4 critical resource types (financial, knowledge, efficiency and intellectual resource) and 6 competence types (relational, collaboration, strategic, innovation, managing and service capability), were performed as the principal factors for value co-creation in the relationship network. A research framework that can analyze the value co-creation phenomena in the relationship network was established.

• Journal of Environmental Health Sciences
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• v.42 no.6
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• pp.375-384
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• 2016

Objectives: This paper analyzes the intersection of tort law and environmental health in a recent court decision. Methods: This paper analyzes Supreme Court Decision 2011Da7437, Decided on September 4, 2014 and related lower court decisions. Results: The plaintiffs sought financial compensation from the defendants, arguing that air pollutants in gases emitted by vehicles produced by the defendants had caused them to acquire respiratory diseases. The district court highlighted the need to mitigate the burden of proof for the plaintiffs, but proceeded to review whether the plaintiffs proved the actual toxicity levels of the air pollutants, whether the defendant's vehicles were the main source of the emissions, the plaintiff's level of exposure to the pollutants, and causation between the emissions and the injury. By doing so, the district court required the plaintiffs to prove both indirect and direct facts of causation, increasing burden of proof for plaintiffs. The appellate court upheld the district court's decision, adding that the defendant's conduct did not constitute an illegal act because it did not violate the emissions standards set by environmental law. The Supreme Court upheld the appellate court's decision, reasoning that the epidemiological evidence cannot establish a direct causation for diseases that lack specificity. Conclusion: This case demonstrates that discussions in environmental health have significance in tort lawsuits. For each fact that the plaintiffs and defendants attempted to prove, environmental health research studies were offered as evidence. In addition, the courts decided the legality of the defendant's conduct based on emission standards set by environmental law.

• Journal of Information Technology Applications and Management
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• v.25 no.1
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• Journal of the Korea Institute of Building Construction
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• v.16 no.4
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• pp.331-339
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• 2016

According to high-rise, complexation, and enlargement of buildings, various construction methods are being developed, and the significance of construction method selection about main work types has emerged as a major interest. However, it has been pointed out that hand-on workers cannot consider project characteristics carefully, and they lack an objective standard or reference for main construction method selection. Hence, the selection is being made depending on hand-on workers' experience and intuition. To solve this problem, various studies have proceeded for construction method selection of main work types using Artificial Intelligence like Fuzzy, AHP and Case-based reasoning. It is difficult to apply many different kinds of construction method selection to every main work type with consideration for characteristics of work types and condition of a construction site when selecting construction method in the field. Accordingly, this study proposed the decision-making model which can apply to fields easily. Using matrix analysis and liner transformation, this study verified consistency of study models applied in the process of soil retaining selection with a case study.

• Economic and Environmental Geology
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• v.18 no.2
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• pp.93-105
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• 1985

Whether a skarn deposit in carbonate host occurs in contact with certain igneous mass or not has been a general criterion in identifying the igneous rock that genetically relates to the skarn deposit. It is well known, however, that there are many skarn deposits which are not close to any given igneous contact but are far away from the contact. In this paper the reason why such deposits can be formed at a distance from the contact as mentioned is expressed based on the concept that skarn deposits and related igneous rocks are genetically connected at depth where ore-forming fluids emanate from magma and are removed upwards; the movement of ore-forming fluids separated from magma at any depth may have a tendency to infiltrate upward in bulk rather than to diffuse laterally; the paths of magma and cogenetic ore-forming fluids may be identical at lower depths but the latter can be diverted from the former with upward movement so that the positions of the skarn deposits which resulted from the ore-forming fludis at upper levels can be distant from the igneous contacts on a given horizontal section. Statistics indicate that the majority of exoskarns are found at distances up to 800 meters or rarely up to 3,000 meters from igneous contacts and endoskarns up to 600 meters or more. Numerous case studies of skarn deposits in various parts of the world support the above reasoning indicating a general downward convergency of skarn orebodies and related igneous masses with depth. A typical example of this situation is well demonstrated at the Keumseong molybdenum deposit, which is apart from the Jecheon granite on the surface but gets closer to the granite body with depth and finally is intertongued with the granite apophyses in its root zone. Another case for skarn deposit not associated with igneous contact either laterally or vertically but with a deep-seated distal igneous mass is the Sangdong scheelite deposit; 700 meters below the scheelite orebody a blind pluton of muscovite granite, which intruded into the Precambrian crystalline schist, has been recently discovered by deep drilling.