• Journal of Intelligence and Information Systems
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• 제20권4호
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Proceedings of the Korea Contents Association Conference
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• 한국콘텐츠학회 2003년도 춘계종합학술대회논문집
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• pp.344-350
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• 2003

In this paper, we propose a novel sharing method ordering the events occurring between users collaborated with the common telecomputing environment. We realize the sharing method with multimedia data to improve the coworking effect using teleprocessing network. This sharing method advances the efficiency of communicating projects such as remote education, tele-conference, and co-authoring of multimedia contents by offering conveniences of presentation, group authoring, common management, and transient event productions of the users. As for the conventional sharing white board system, all the multimedia contents segments should be authored by the exclusive program, and we cannot use any existing contents or program. Moreover we suffer from the problem that ordering error occurs in the teleprocessing operation because we do not have any line-up technology for the input ordering of commands. Therefore we develop a method of retrieving input and output events from the windows system and the message hooking technology which transmits between programs in the operating system In addition, we realize the allocation technology of the processing results for all sharing users of the distributed computing environment without any error. Our sharing technology should contribute to improve the face-to-face coworking efficiency for multimedia contents authoring, common blackboard system in the area of remote educations, and presentation display in visual conference.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제19권4호
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• pp.481-491
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• 2018

As the global business environment is dynamic, uncertain, and complex, supply chain management determines the performance of the supply chain in terms of the utilization of resources and capabilities of companies involved in the supply chain. Companies pursuing open innovation gain greater access to the external environment and accumulate knowledge flows and learning experiences, and may generate better business performance from dynamic capabilities. This study analyzed the effects of supply chain dynamic capabilities, open innovation, and supply uncertainty on supply chain performance. Through questionnaires on 178 companies listed on KOSDAQ, empirical results are as follows: First, integration and reactivity capabilities among supply chain dynamic capabilities have a positive effect on supply chain performance. Second, the moderating effect of open innovation showed a negative correlation in the case of information exchange, and a positive correlation in the cases of integration, cooperation and reactivity. Third, two of the 3-way interaction terms, "information exchange*open innovation*supply uncertainty" and "integration*open innovation*supply uncertainty" were statistically significant. The implications of this study are as follows: First, as the supply chain needs to achieve optimization of the whole process between supply chain components rather than individual companies, dynamic capabilities play an important role in improving performance. Second, for KOSDAQ companies featuring limited capital resources, open innovation that integrates external knowledge is valuable. In order to increase synergistic effects, it is necessary to develop dynamic capabilities accordingly. Third, since resources are constrained, managers must determine the type or level of capabilities and open innovation in accordance with supply uncertainty. Since this study has limitations in analyzing survey data, it is necessary to collect secondary data or longitudinal data. It is also necessary to further analyze the internal and external factors that have a significant impact on supply chain performance.

• Journal of The Korean Association For Science Education
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• 제19권3호
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• pp.409-427
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• 1999

The aim of this study was to illustrate how STS-related ideas in science education have been developed historically in the context of British education, particularly focused on the period of the 19th century and the first half of the 20th century. It has been hardly considered that the basic ideas of the STS education, one of the two paradigms of current science education together with constructivism, can be traced back to the beginning of the school science education itself. far beyond some of the programs which are largely regarded as the first-developed STS programs in Britain, such as Science in Society and SISCON. The movement of Mechanics' Institute during the first half of the 19th century would be the first systematic attempt to bridge the gap between the knowledge of pure science and its practical applications, although the main target was working-class adults rather than school pupils. At the end of the first half of the 19th century, this application-focused approach of science teaching was echoed in the elementary schools by Richard Dawes, one of the early experimenters of school science. The second half of the century was in large the period of the establishment of science as one of the core elements of school curriculum, mainly by emphasizing the aspect of pure science as a means for mental training. During this period, the elements of STS education-related appeared in the subject called 'Object Lesson' in elementary schools which was practically a separate subject from those of science. After the turn of the century, triggered by the experience of World War I, the growing appreciation of the impacts of science upon society and of the necessity of the teaching of science for wider audience gave a great impact towards two new main movements, i.e. for General Science and Citizen Science. The later illustrates a typical example of the STS movement in school science during the first half of the 20th century, particularly driven by the socialistic ideas towards the relation between science and society.

• Journal of Korean Public Health Nursing
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• 제19권1호
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• pp.74-94
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• 2005

The purpose of this study was to compare the effect of education between CAI(Computer Assisted Instruction) and lectures for smoking cessation among male students who attended vocational high schools. Conducted from February 24th to April 26th, 2003, the study design was quasi-experimental with nonequivalent control group pretest-posttest design. The study subjects were 60 male students in K vocational high school in Daegu city, who were present smokers and had more than 7.0 ppm concentration level of carbon monoxide. Thirty students were randomly chosen as the experimental group which applied CAI education method for smoking cessation. The other 30 students served as the control group which received lecture education method of 40 minutes on four consecutive days. CAI education for smoking cessation was composed of ready-made individual learning contents, counseling by using cyber-communication, writing a letter to stop smoking, and writing a written agreement for smoking cessation. Lecture education for smoking cessation was composed of a ready-prepared lecture for the group, writing a letter to stop smoking, and writing a written agreement for smoking cessation. To measure smoking related knowledge, Jeong Ree Roh(1996)'s smoking related knowledge scale$(Cronbach's\;{\alpha}=0.84)$ was modified and used by the researcher. To measure smoking related attitude, Jeong Ree Roh(1996)'s smoking related attitude scale$(Cronbach's\;{\alpha}=0.91)$ was modified and used by the researcher. Smoking related knowledge scale's Cronbach's $\alpha$ was 0.83 in the pilot study and 0.93 in this study. Smoking related attitude scale's Cronbach's a was 0.80 in the pilot study and 0.98 in this study. To determine the smoking amount, the number of cigarettes smoked per day was checked. The concentration level of CO in the exhaled breath was measured (Micro CO Cat. No. MCO2, UK). Data was analyzed by $x^2-test$, t-test, repeated measures ANOVA. simple main effects, and time contrast test with SPSS/Win 11.0 program. The results of this study were as follows: 1. The first hypothesis. that 'Smoking-related knowledge score in the experimental group by using CAI education for smoking cessation will be higher than that in the control group by using lecture education for smoking cessation', was not supported. 2. The second hypothesis, that 'Smoking-related attitude in the experimental group by using CAI education for smoking cessation will be higher than that in the control group by using lecture education for smoking cessation'. was supported(F=6490.79. p=0.000). 3. The third hypothesis. that 'Smoking amount in the experimental group by using CAI education for smoking cessation will be less than that in the control group by using lecture education for smoking cessation'. was supported. 1) The third-1st sub-hypothesis. that 'The number of cigarettes smoked per day in the experimental group by using CAI education for smoking cessation will be less than that in the control group by using lecture education for smoking cessation'. was supported(F=134.19. p=0.000). 2) The third-2nd sub-hypothesis. that 'The concentration level of CO by ppm per one exhaled breath in the experimental group by using CAI education for smoking cessation will be lower than that in the control group by using lecture education for smoking cessation"' was supported(F=268.55. p=0.000). From the above results. CAI education can be an effective intervention to improve smoking-related knowledge and attitude. and to reduce the number of cigarettes smoked per day and the concentration level of CO by ppm per one exhaled breath. Lecture education can be effective to improve smoking-related knowledge. In the future, when CAI education and lecture education for smoking cessation are applied on the school nursing field. the students can gain a comprehensive understanding of smoking cessation, changes in smoking-related knowledge. smoking-related attitude and reducing smoking amount. Furthermore, CAI education for smoking cessation could be developed as an individual self initiative program and could give a guideline to apply CAI education for smoking cessation in other field.

• Korean Journal of Child Education & Care
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• 제18권4호
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• pp.247-265
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• 2018

Objective: The purpose of this study was to investigate the effect of teacher support program for integration of handicapped children on teaching efficacy of daycare center teachers. Methods: In the study, 12 day care teachers in 4 day care centers in Seoul and Gyeonggi area were selected as experimental groups and 12 teachers in 5 day care centers were selected as control group. Teacher education is carried out through group education, such as understanding of developmental area, curriculum modification, activity-based embedded intervention, cooperative learning, direct teaching, disability understanding education, behavior support, family support. Individual teacher education provided counseling on the reality of child care for children with disabilities that reflects the needs of teachers for integrated child care for handicapped children. Teacher's Efficacy in Inclusive Practices (TEIP) was used as a pre post test to measure teacher's efficacy change. In order to analyze the results of the study, two independent sample t tests were conducted on the difference between pre-post test of teacher efficacy between the two groups. Results: As a result, There was a significant difference in the pre-post change of teacher efficacy between the two groups. Conclusion/Implications: The results of this study are as follows, teacher support program provided immediate feedback in integrated child daycare center for the handicapped children, child care teachers improved their integrated handicapped children care expertise, provided responsive teacher support program to the actual needs of the site, teacher support program reflected various variables related to integration, and emphasized the cooperative relationship between researcher and child daycare center teacher. The results of this study can be used as actual data of field where lack of support for the integration of handicapped children is lacking.

• Journal of the Korean Institute of Landscape Architecture
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• 제49권6호
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• pp.49-63
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• 2021

The Forest Visitor Centers for Little Children who means preschoolers is an educational facility that achieves holistic growth by experiencing forests, and it should not be completed by installing specific facilities in the forest environment, but should be a space where preschoolers can play freely in the forest environment themselves. This study comprehensively evaluated the current status of Seoul Forest Visitor Centers for Little Children and suggested space improvement measures to enhance the effectiveness of forest experience. Through the theoretical review, seven spatial elements that enhance the effect of forest experience and six areas composing outdoor play areas were derived to prepare an analysis table for current status evaluation, and field survey studies were conducted on 24 centers in Seoul. Through expert interviews, the physical status was examined from the perspective of childhood education and the experiences of the users were summarized. As a result of the study, the Seoul Forest Visitor Center for Little Children is classified into six types according to the location characteristics and spatial structure, and has the characteristics of each type. The effectiveness of forest experience can be enhanced by identifying and revealing the environmental strengths of individual centers. In the case of outdoor experience learning zones, the proportion of exercise play areas was very large. By evenly organizing the forest experience space for each area, it will be possible to provide more diverse experiences to preschoolers. However, the status of uniform facility-oriented cannot be viewed as a fragmentary factor that lowers the effect of forest experience. The key to increasing the effect of forest experience by inducing creative activities is the spatial composition that considers the surrounding natural environment. Facilities should be a medium to help preschoolers' interest move into the forest. This study prepared data to understand the average physical status of the Seoul Forest Visitor Center for Little Children and suggested space improvement measures to increase the effectiveness of forest experience. This can be used as basic data for research to improve the quality level of the Seoul Forest Visitor Center for Little Children about 10 years after the project was implemented.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Journal of Korea Water Resources Association
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• 제54권spc1호
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• The Mathematical Education
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• 제62권3호
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.