• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• 한국노년학
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• v.30 no.3
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• pp.973-992
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• 2010

The hearing impairment is the representative disorder that affects the quality of the routine life of the aged period. This study was aimed to develop the Korean evaluation scale for hearing handicap(KESHH) with which we can evaluate social and psychological effects of the hearing impairment. Applying this scale clinically, we can analyze the geriatric hearing loss specifically and improve the quality of the aural rehabilitation that can help the hardness of the hearing impairment. Data were collected from 288 participants(176 hearing aid users and 112 non-hearing aid users) and the average age of the participants was 67.4 years old ( 60.15 for the hearing aids users and 78.9 for the non hearing users). The composition ratio of the male and female participants were 58.0% and 42.0% and extrovert and introvert personality were 49.3% and 50.7% showing balanced formation. The tentative draft of KESHH measurements were produced with 30 items and following 5 subscales. Using factor analysis, 6 items were erased and 4 subscales - social effect, psycho/emotional effect, interpersonal effect, and perception of hearing aids - were identified. As each subscale consisted of 6 items, 24 items were corrected and remained totally. Conclusively, the KESHH was developed with 24 items and 4 subscales including 6 items on each subscale. In addition, the KESHH was divided into type-1 and 2 depending on hearing aid users and non hearing aid users. The results of this study can be summarized as the following 5 parts. Firstly, the reliabilities of the KESHH were proved to be high because the subscales' Cronbach alpha values were from 0.723 through 0.895. Secondly, the KESHH showed systematically increasing score as the hearing impairment increased. The lowest score was 24 and the highest score was 117 and the average scores of the hearing impaired and non-hearing impaired are 72.06(SD=15.67) and 66.98(SD=20.94) showing 5.08 increased score for the hearing impaired. Depending on the degree of the hearing loss, the scores recorded 52.63 at the below of the mild hearing loss, 67.29 for the moderate hearing loss, 71.89 for the moderately severe hearing loss, and 75.57 for the severe hearing loss The comparison of the scores by hearing levels indicated that the higher the hearing levels were, the higher the scores of the KESHH with statistical significance(p<0.001). Thirdly, the correlation among 4 subscales was 0.384~0.880(p<0.001). Also, the pure tone average, personality, and the four subscales correlations showed statistical significance with 0.148~0.880 except for the pure tone average and personality and the pure tone average and perception of hearing aids. Fourthly, the total variances explained for the independent subscles were analyzed with multiple regression. The social effect was explained 17.4% with pure tone average, personality, and the status of hearing aid use variances. The psycho/emotional effect was explained 14.4% with puretone average, personality, and age variances. The interpersonal effect was explained 11.2% with pure tone average, personality, and the status of hearing aid use variances. The perception of hearing aids effect was explained 2.2% with only personality. Finally, test-retest reliability was proved to be high with 0.791(p<0.001). Conclusively, the KESHH that was developed considering Korean culture can be a useful instrument for expressing the hearing handicaps of the Korean aged hearing impaired in scores for both hearing aid users and non-users. Also, it is thought that the KESHH is useful clinically for identifying the changes of the hearing handicap scores before and after wearing hearing aids and aural rehabilitation at diverse situations.

• (The) Research of the performance art and culture
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• no.23
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• pp.281-312
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• 2011

The aim of this study is to examine various life conditions of actors of Joseon periods in unofficial historical stories. Yadam Literature(Korean unofficial historical stories) had been collected Sadaebu(the past Korean nobility and Confucian intelligentsia) among the people that stories had been handed down orally. and they had been wrote them. So Yadam Literature was heterozygous between the folk culture and the ruling class. And it was mixed and adapted legends and folktales, adding literary imagination. had a decisive role to cultivating novel that owed much to prosaic inspiration during A. D. 18~19. Besides, set a high value on excellent novel itself. Yadam Literature had a verisimilitude because it described a contemporary reality as it was founded on freely prosaic inspiration. In those days, so called Suchok and Seunggwangdae had performed Uhee(a comic theatrical performance) in Joseon periods. Suchok was the lowest class of people and Seunggwangdae was performing Buddhist monk in that time. Uhee had performed three kinds of comedies. One satirized and insinuated kings. Other satirized corrupt officials, too. Another had mimic everything. It is famous at that time as a king knew repertoire. Confucian scholars very were fond of Uhee in those ages. Because they favored a criticism of Uhee's satire. They thought that it gave people good lesson or instruction. Heri Bergson said that comic and Humor included lesson. At that time, those thought were universal in the world whether east or west. At any rate, I classify six kinds of types Uhee in Yadam Literature. First, satirizing and accusing corrupt officials. Second, an actor who use a satire in order to appeal secure a government position of his lord to a king. Third, shamans and actors who use a satire in order to appeal sufferings themselves to a king. Forth, actors and performing Buddhist monks that skillfully mimic anything. Fifth, describing actor's extremely miserable life. Sixth, wit and humor of actors. The contents of Uhee were various. Korean traditional actors adeptly dealt with aspects comic of wit, satire, humor, etc. Sometimes they used changeable transition them. By doing that, a great number of people enjoyed fully the sense of freedom. Korean traditional actors were the lowest class of people. They had lived extremely miserable life. But they had been exist as actions, interactions, and relationship in society those days. they were not only open to people, but also might foster community to peoples.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• (The)Study of the Eastern Classic
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• no.70
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• pp.435-466
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• 2018

"Hajaeilgi (荷齋日記)" was written by Ji Gyu-sik, a gongin of Saongwon (司饔院)'s branch, almost everyday for 20 years and 7 months from January 1st, 1891 until the leap month of June 29th, 1911. It deals with many different areas including domestic and foreign circumstances, custom, rituals, all the affairs related to the branch, and also everyday life. Particularly, Ji Gyu-sik did not belong to the yangban class, and we can hardly find diaries written by such class' people. Here, what this author pays attention to among the things written in "Hajaeilgi" is the contents about rituals, especially coming of age, wedding, funeral and ancestral rites. Ji Gyu-sik did write in his "Hajaeilgi" about coming of age, wedding, funeral and ancestral rites that were actually performed then as a person not belonging to the yangban class. Such diaries are very rare, and its value is highly appreciated as a material. Particularly, from the late 19th to the early 20th century of this author focuses on the a study of coming of age, wedding, funeral and ancestral rites as we can see some aspects about it from his diary. Coming-of-age rites were carried out in the first month of the year generally, and in this period, we can see the transformation of their performing period as it was diversified then. This was not exceptional in yangban families. About wedding, while it was discussed, it came to be canceled more often than before maybe because they were going through the process of enlightenment then. It seems that choosing the day was not done in the bride's family always. Jungin or commoners had a weeding in the bride's house, but when it was needed, it was also performed in the groom's house. Ji Gyu-sik followed the traditional wedding procedure for his children rather faithfully, but it was applied flexibly according to the two families' situations or conditions. Ignoring the traditional manners, they had a wedding in the period of mourning or performed a wedding in the groom's house bringing the bride there. It seems that this was related to the decline of Confucian order in the society in the process of modernization. Also, the form of donations changed, too. Gradually, it was altered to the form of money gifts. Moreover, unlike before, divorcing seems to have been allowed then. Remarriage or divorce was the custom transformed from before. Funeral rites had different durations from death up to balin (carrying out a bier for burial) and hagwan (lowering a coffin into the grave), and so it means that they also went through transformation. Sa-daebu used usually 3 months but here was 7 days from death to balin normally, but it seems that there were yangban families not following it. The traces of 3-iljang (burial on the third day after death) most commonly found these days and chowoo jaewoo samwooje can be also found in "Hajaeilgi". Such materials are, in fact, very highly evaluated nowadays. Meanwhile, donations also changed gradually to the form of money. Regarding ancestral rites, time for memorial service was not fixed. Ji Gyu-sik did not follow jaegye (齋戒) before carrying out gijesa, and in some worse case, he went to pub the day before the memorial service to meet his lover or drink. This is somewhat different from the practice of yangban sadaebu then. Even after entering Christianity, Ji Gyu-sik performed memorial service, and after joining Cheondogyo, he did it, too. Meanwhile, there were some exceptions, but in Hansik or Chuseok, Ji Gyu-sik performed charye (myoje) before the tomb in person or sent his little brother or son to do it. But we cannot find the contents that tell us Ji Gyu-sik carried out myoje in October. Ji Gyu-sik performed saengiljesa calling it saengsincharye almost every year for his late father. But it is noticeable that he performed saengsincharye and memorial service separately, too, occasionally. The gijesa, charye, myoje, and saengsincharye carried out by jungin family from Gyeonggi Gwangju around the time that the status system was abolished and the Japanese Empire took power may have been rather different and less strict than yangban family's practice of ancestral rites; however, it is significant that we can see with it the aspects of ancestral rites performed in family not yangban. As described above, the contents about the a study of coming of age, wedding, funeral and ancestral rites found in "Hajaeilgi" are equipped with great value as material and meaningful in the perspective of forklore.