• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.287-316
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• 2023

As global energy crisis and the COVID-19 pandemic have emerged as social issues, there is a growing demand for companies to move away from profit-centric business models and embrace sustainable management that balances environmental, social, and governance (ESG) factors. ESG activities of companies vary across industries, and industry-specific weights are applied in ESG evaluations. Therefore, it is important to develop strategic management approaches that reflect the characteristics of each industry and the importance of each ESG factor. Additionally, with the stance of strengthened focus on ESG disclosures, specific guidelines are needed to identify and report on sustainable management activities of domestic companies. To understand corporate sustainability strategies, analyzing ESG reports and news articles by industry can help identify strategic characteristics in specific industries. However, each company has its own unique strategies and report structures, making it difficult to grasp detailed trends or action items. In our study, we analyzed ESG reports (2019-2021) and news articles (2019-2022) of six companies in the 'Finance,' 'Manufacturing,' and 'IT' sectors to examine the sustainability strategies of leading domestic ESG companies. Text mining techniques such as keyword frequency analysis and topic modeling were applied to identify industry-specific, ESG element-specific management strategies and issues. The analysis revealed that in the 'Finance' sector, customer-centric management strategies and efforts to promote an inclusive culture within and outside the company were prominent. Strategies addressing climate change, such as carbon neutrality and expanding green finance, were also emphasized. In the 'Manufacturing' sector, the focus was on creating sustainable communities through occupational health and safety issues, sustainable supply chain management, low-carbon technology development, and eco-friendly investments to achieve carbon neutrality. In the 'IT' sector, there was a tendency to focus on technological innovation and digital responsibility to enhance social value through technology. Furthermore, the key issues identified in the ESG factors were as follows: under the 'Environmental' element, issues such as greenhouse gas and carbon emission management, industry-specific eco-friendly activities, and green partnerships were identified. Under the 'Social' element, key issues included social contribution activities through stakeholder engagement, supporting the growth and coexistence of members and partner companies, and enhancing customer value through stable service provision. Under the 'Governance' element, key issues were identified as strengthening board independence through the appointment of outside directors, risk management and communication for sustainable growth, and establishing transparent governance structures. The exploration of the relationship between ESG disclosures in reports and ESG issues in news articles revealed that the sustainability strategies disclosed in reports were aligned with the issues related to ESG disclosed in news articles. However, there was a tendency to strengthen ESG activities for prevention and improvement after negative media coverage that could have a negative impact on corporate image. Additionally, environmental issues were mentioned more frequently in news articles compared to ESG reports, with environmental-related keywords being emphasized in the 'Finance' sector in the reports. Thus, ESG reports and news articles shared some similarities in content due to the sharing of information sources. However, the impact of media coverage influenced the emphasis on specific sustainability strategies, and the extent of mentioning environmental issues varied across documents. Based on our study, the following contributions were derived. From a practical perspective, companies need to consider their characteristics and establish sustainability strategies that align with their capabilities and situations. From an academic perspective, unlike previous studies on ESG strategies, we present a subdivided methodology through analysis considering the industry-specific characteristics of companies.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.10 no.1
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• pp.21-33
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• 1999

The clinical assessment for preschool children who are known to have problems in selfreporting tends to be dependent on outsiders' reporting. Thus, the direct assessment of children's inner experience, thoughts and feelings is difficult. MacArthur Story-Stem Battery(MSSB) developed to learn more about preschool children's mental representation in play is used in this study to help assess clinical preschool children through developmental study of normal children's mental representation. Fifty five children(32boys and 23girls) who performed MSSB, IQ Test, Peabody Picture Vocabulary Test-Revised(PPVT) were videotaped and were analyzed. The results of this study were as follows：1) Children frequently displayed negative mental representation such as atypical negative response, reparation/guilt, punishment, personal injury and so on during emotionally laden play situation. 2) Mental representation of parent appeared positive, disciplinary, and negative in respective. 3) As a result of factor analysis of MSSB content themes, aggressive, prosocial, and oppositional content theme composites were generated. Aggressive content included atypical negative response, aggression, personal injury, and exclusion. Prosocial content included affection, affiliation, and reparation/guilt. Oppositional content included punishment and non-compliance. 4) Mental representation of parent and content themes showed significant correlation. Positive, negative, and disciplinary representation were significantly correlated for prosocial(r=0.40), aggressive (r=0.52), and oppositional(r=0.75) content theme respectively. 5) Among the correlations between parental mental representations and emotional responses, positive parental representation and anxiety showed significant negative correlation(r=-0.43). 6) Among the correlations between content themes and emotional responses, there were significant positive correlations between aggressive(r=0.28) and oppositional content themes(r=0.29) and distress, and were significant negative correlations between prosocial content theme and concern(r=-0.29) and anxiety(r=-0.43). According to the above results, preschool children frequently displayed negative mental representation in emotionally conflictual play situation. Children with more prosocial themes in their stories exhibit more positive parental mental representation. Also, children with more aggressive themes tend to display more negative parental representation and negative emotional responses.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.14 no.1
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• pp.12-25
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• 2003

Objectives：As increasing number of new antidepressants have been being introduced in clinical practice, pharmacological understanding has been broadened. These changes mandate new information and theories to be incorporated into the treatment process of children with depressive disorders. In light of newly coming knowledge, this review intended to recapitulate the characteristics of new antidepressants and to consider the pivotal issues to develope guidelines for the treatment of depression in childhood and adolescence. Methods：Searching the Pub-Med online database for the articles with the key words of 'new', 'antidepressants' and 'children' ninety-seven headings of review articles were obtained. The author selected the articles of pertinent subjects in terms of either treatment guideline or psychopharmacology of new antidepressants. When required, articles about the clinical effectiveness of individual antidepressants were separatedly searched. In addition, the safety information of new antidepressants was acquired by browsing the official sites of the United States Food and Drugs Administration and Department of Health and Human Services. Results：1) For the clinical course, treatment phase, and treatment outcome, the reviews or treatment guidelines adopted the information from adult treatment guidelines. 2) Systematic and critical reviews unambiguously concluded that selective serotonin reuptake inhibitors(SSRIs) excelled tricyclic antidepressants( TCAs) for both efficacy and side effect profiles, and were recommend for the first-line choice for the treatment of children with depressive disorders. 3) New antidepressants generally lacked treatment experiences and randomized controlled clinical trials. 4) SSRIs and other new antidepressants, when used together, might result in pharmacokinetic and/or pharmacodynamic drug-to-drug interaction. 5) The difference of the clinical effectiveness of antidepressants between children and adults should be addressed from developmental aspects, which required further evidence. Conclusion：Treatment guidelines for the pharmacological treatment of childhood and adolescence depression could be constructed on the basis of clinical trial findings and practical experiences. Treatment guidelines are to best serve as the frame of reference for a clinician to make reasonable decisions for a particular therapeutic situation. In order to fulfill this role, guidelines should be updated as soon as new research data become available.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.236-251
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• 2020

As remote sensing technologies are evolving, and more satellites are orbited, the demand for using satellite data for disaster monitoring is rapidly increasing. Although natural and social disasters have been monitored using satellite data, constraints on establishing an integrated satellite-based near real-time disaster monitoring system have not been identified yet, and thus a novel framework for establishing such system remains to be presented. This research identifies constraints on establishing satellite data-based near real-time disaster monitoring systems by devising and testing a new conceptual framework of disaster monitoring, and then presents a feasible disaster monitoring system that relies mainly on acquirable satellite data. Implementing near real-time disaster monitoring by satellite remote sensing is constrained by technological and economic factors, and more significantly, it is also limited by interactions between organisations and policy that hamper timely acquiring appropriate satellite data for the purpose, and institutional factors that are related to satellite data analyses. Such constraints could be eased by employing an integrated computing platform, such as Amazon Web Services(AWS), which enables obtaining, storing and analysing satellite data, and by developing a toolkit by which appropriate satellites'sensors that are required for monitoring specific types of disaster, and their orbits, can be analysed. It is anticipated that the findings of this research could be used as meaningful reference when trying to establishing a satellite-based near real-time disaster monitoring system in any country.

• Journal of Korean Elementary Science Education
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• v.25 no.spc5
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• pp.556-566
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• 2007

The purpose of this study was to develop an electroencephalogram (EEG) based differential-scale for scientifically gifted students in elementary school. For this study, signals of EEG with 19 channels were recorded during the generation of our scientific hypothesis using 22 scientifically gifted students, and with 49 average students being used as the control group. IQ, TCT and knowledge generation (KG) as constructs of the scientifically gifted were administered for both the scientifically gifted and the normal, control group elementary students. A 'gifted' value was added to paper test scores of the IQ, TCT, and KG constructs in order to make a personal standardization score for the gifted students. As a dependent variable, the groups were divided by means of the standardization scores thus produced and as an autonomous variable, various EEG parameters were presented through linear analysis, nonlinear analysis, and interdependency measures of the EEG. Multiple linear regression analysis was applied successfully to explain the EEG parameters and to show the characteristics of the scientifically-gifted. The discrimination analysis was administered through the results of multiple linear regression of the EEG parameters thus produced. This study represents the foundation of the development of an EEG based discriminant-scale for scientifically gifted students in elementary school, because it will be able to faithfully discriminate between scientifically-gifted and average students. The results of this study indicates that most of the EEG parameters produced can contribute to predicting the characteristics of the scientifically-gifted in that they express the degree of mutual information and the coherence of mutuality. Accordingly, mutual connectivity which appears to originate in the brain seems to the core of discrimination.