• Journal of Korea Entertainment Industry Association
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• v.13 no.5
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• pp.263-271
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• 2019

The recent phenomenon of job "Miss Match", which is inconsistent with knowledge in the demand of educational training institutes and industries, has spread to an increase in private education costs for reeducation and employment of new hires, resulting in weak individual job competency and poor employment capability, as well as economic and material waste at the national level. To compensate for these problems, the National Competency Standards(NCS), which are available immediately in practice and look for a standard point of national job competency with the aim of fostering human resources sought by industries, were developed, and even the NCS-based qualification system was launched in line with the stream of times. This study is intended to look into the importance and priority of competency units and competency unit elements at the NCS-based qualification L3 level in the skin care field for an overall check of the NCS-based qualification level at a time when educational institutes are organizing and operating the school curriculums according to the NCS and NCS-based qualification level. And it is attempted to provide basic data for the development of curriculum in fostering professional human resources required by industries. To analyze the needs for competency units and competency unit elements at the L3 level, a survey using AHP method was carried out to a group of field experts and a group of education experts. In addition, the SPSS(Statistical Package for Social Science) ver. 21.0 and Expert Choice 2000, an AHP-only solution was used to do statistical processing through the processes of data coding and data cleaning. The findings showed that there was a partial difference of opinion between a group of field experts and a group of education experts. This indicates that the inconsistencies between educational training institutes and industrial sites should be resolved at this time of change with the aim of fostering field customized human resources with professional skills. Consequently, the solution is to combine jobs at industrial sites and standardized educations of educational institutes with human resources required at industrial sites.

• Journal of Chest Surgery
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• v.38 no.1 s.246
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• pp.44-49
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• 2005

Although thymomas are relatively common mediastinal tumors, to date not only has a universal system of pathologic classification not been established but neither has a clearly defined predictable relationship between treatment and prognosis been made. Recently, a new guideline for classification was reported by WHO, and efforts, based on this work, have been made to better define the relationship between treatment and pro­gnostic outcome. In the present study a comparative analysis between the WHO classification and Masaoka stage system with the clinical disease pattern was conducted. Material and Method: A total of 98 patients undergoing complete resection for mediastinal thymoma between Juanuary 1993 and June 2003 were included in the present study. The male female ratio was 48 : 50 and the mean age at operation was $49.6{\pm}13.9\;years.$ A retrospective analytic comparison studying the relationship between the WHO classification and the Masaoka stage system with the clinical disease pattern of thymoma was conducted. Pathologic slide specimens were carefully examined, details of postoperative treatment were documented, and a relationship with the prognostic outcome and recurrence was studied. Result: There were 7 patients in type A according to the WHO system of classification, 14 in AB, 28 in B 1, 23 in B2, 18 in B3, and 9 in type C. The study of the relationship between the Masaoka stage and WHO classification system showed 4 patients to be in WHO system type A, 7 in type AB, 22 in B 1, 17 in B2, and 3 in type B3 among 53 $(54{\%})$ patients shown to be in Masaoka stage I. Among 28 $(28.5{\%})$ patients in Masaoka stage II system, there were 2 patients in type A, 7 in AB, 4 in B 1, 2 in B2, 8 in B3, and 5 in type C. Among 15 $(15.3{\%})$ in Masaoka stage III, there were 1 patient in type B1, 3 in B2, 7 in B3, and 4 in type C. Finally, among 2 $(2{\%})$ patients found to be in Masaoka stage IV there was 1 patient in type B1, and 1 in type B2. The mean follow up duration was $28{\pm}6.8$ months. There were 3 deaths in the entire series of which 2 were in type B2 (Masaoka stages III and IV), and 1 was in type C (Masaoka stage II). Of the patients that experienced relapse, 6 patients remain alive of which 2 were in type B2 (Masaoka III), 2 in type B3 (Masaoka I and III) and 2 in type C (Masaoka stage II). The 5 year survival rate by the Kaplan-Meier method was $90{\%}$ for those in type B2 WHO classification system, $87.5{\%}$ for type C. The 5 year freedom from recurrence rate was $80.7{\%}$ for those in WHO type B2, $81.6{\%}$ for those in type B3, and $50{\%}$ for those in type C. By the Log-Rank method, a statistically significant correlation between survival and recurrence was found with the WHO system of classification (p<0.05). An analysis of the relationship between the WHO classification and Masaoka stage system using the Spearman correction method, showed a slope=0.401 (p=0.023), showing a close correlation. Conclusion: As type C of the WHO classification system is associated with a high postoperative mortality and recurrence rate, aggressive treatment postoperatively and meticulous follow up are warranted. The WHO classification and Masaoka stage system were found to have a close relationship with each other and either the WHO classification method or the Masaoka stage system may be used as a predict prognostic outcome of Thymoma.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.