• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.5
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• pp.265-277
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• 2019

Using the measured data of waves and shore-line, we reviewed the grand circulation process and seasonal variation of beach cusp at the Mang-Bang beach from the perspective of trapped mode Edge waves known as the driving mechanism of beach cusp. In order to track the temporal and spatial variation trends of beach cusp, we quantify the beach cusp in terms of its wave length and amplitude detected by threshold crossing method. In doing so, we also utilize the spectral analysis method and its associated spectral mean sand wave number. From repeated period of convergence and ensuing splitting of sand waves detected from the yearly time series of spectral mean sand wave number of beach cusp, it is shown that the grand circulation process of beach cusp at Mang-Bang beach are occurring twice from 2017. 4. 26 to 2018. 4. 20. For the case of beach area, it increased by $14,142m^2$ during this period, and the shore-line advanced by 18 m at the northen and southern parts of the Mang-Bang beach whereas the shore-line advanced by 2.4 m at the central parts of Mang-Bang beach. It is also worthy of note that the beach area rapidly increased by $30,345m^2$ from 2017.11.26. to 2017.12.22. which can be attributed to the nature of coming waves. During this period, mild swells of long period were prevailing, and their angle of attack were next to zero. These characteristics of waves imply that the main transport mode of sediment would be the cross-shore. Considering the facts that self-healing capacity of natural beaches is realized via the cross-shore sediment once temporarily eroded. it can be easily deduced that the sediment carried by the boundary layer streaming toward the shore under mild swells which normally incident toward the Mang-Bang beach makes the beach area rapidly increase from 2017.11.26. to 2017.12.22.

• Journal of the Korean Society of Radiology
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• v.13 no.5
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• pp.801-809
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• 2019

Existing Gamma Knife Radiosurgery(GKRS) for large lesions is often conducted in stages with volume or dose partitions. Often in case of volume division the target used to be divided into sub-volumes which are irradiated under the determined prescription dose in multi-sessions separated by a day or two, 3~6 months. For the entire course of treatment, treatment informations of the previous stages needs to be reflected to subsequent sessions on the newly mounted stereotactic frame through coordinate transformation between sessions. However, it is practically difficult to implement the previous dose distributions with existing Gamma Knife system except in the same stereotactic space. The treatment area is expanding because it is possible to perform the multistage treatment using the latest Gamma Knife Platform(GKP). The purpose of this study is to introduce the image-coregistration based on the stereotactic spaces and the strategy of multistage GKRS such as the determination of prescription dose at each stage using new GKP. Usually in image-coregistration either surgically-embedded fiducials or internal anatomical landmarks are used to determine the transformation relationship. Author compared the accuracy of coordinate transformation between multi-sessions using four or six anatomical landmarks as an example using internal anatomical landmarks. Transformation matrix between two stereotactic spaces was determined using PseudoInverse or Singular Value Decomposition to minimize the discrepancy between measured and calculated coordinates. To evaluate the transformation accuracy, the difference between measured and transformed coordinates, i.e., ${\Delta}r$, was calculated using 10 landmarks. Four or six points among 10 landmarks were used to determine the coordinate transformation, and the rest were used to evaluate the approaching method. Each of the values of ${\Delta}r$ in two approaching methods ranged from 0.6 mm to 2.4 mm, from 0.17 mm to 0.57 mm. In addition, a method of determining the prescription dose to give the same effect as the treatment of the total lesion once in case of lesion splitting was suggested. The strategy of multistage treatment in the same stereotactic space is to design the treatment for the whole lesion first, and the whole treatment design shots are divided into shots of each stage treatment to construct shots of each stage and determine the appropriate prescription dose at each stage. In conclusion, author confirmed the accuracy of prescribing dose determination as a multistage treatment strategy and found that using as many internal landmarks as possible than using small landmarks to determine coordinate transformation between multi-sessions yielded better results. In the future, the proposed multistage treatment strategy will be a great contributor to the frameless fractionated treatment of several Gamma Knife Centers.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.492-503
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• 2020

This study was conducted to analyze stem decay in Quercus mongolica and Quercus variabilis in Korea. To ensure even allocation, a total of 5,005 sample trees (2,504 Q. mongolica and 2,501 Q. variabilis) were cut and collected in five regions and 27 subregions. The trees were then examined for stump decay and assigned to four classes based on the degree of scar, tissue decay and decolorization, splitting, and tree hollowing. The results show that the decay rate of Q. mongolica was 66.1%, at least twice as high as that of Q. variabilis, which was rated at 35% (χ2 = 631.15, p < 0.001). The comparison among regions indicated that the highest ratio of Q. mongolica occurs in the Central Regional Forest Service zone (76.5%), followed by the Northern zone (74.8%) and Eastern zone (65.7%). In contrast, the greatest proportion of Q. variabilis is found in the Northern Regional Forest Service zone (38.6%), followed by the Southern (32.9%) and Eastern (37.8%) zones. A statistically significant difference was seen among the five zones (p < 0.05, p < 0.001). There was also a clear tendency for the proportions for the two species to increase with a rise in the DBH. With respect to age, however, a statistically significant difference was found (p < 0.01, p < 0.05) only in Q. mongolica, whose rate increased with the increase in age. Our results show that as the DBH and age increases, the conditions of tissue decay and decolorization are manifested in Q. mongolica, whereas scars are common in Q. variabilis.

• (The)Study of the Eastern Classic
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• no.68
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• pp.539-570
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• 2017

This article is for considering and looking through the meaning of Classic of Poetry(Shi jing) Hiku-ka of Kobayashi Issa before reviewing and adapting Classic of Poetry(Shi jing) in Eastern Asia. Issa wrote his works by using Hyanghwa-Gucheop in 1803, and he had adopted it as his creative works of Hikai absorbing Classic of Poetry(Shi jing) for about half a year. There has been no national study about this so far, and this study covered the aspects of Issa's adapting way of Classic of Poetry(Shi jing) in Japan and China. There have been several problems that the contents were limited to Guo-feng and there were no agreement of terminology as well among researchers. To overcome these limitations, therefore, this article aimed at all the works, rejected the view point as just a translation, and denominated this study as Haiku-ka. Above all, this study looked though Issa's Classic of Poetry(Shi jing) by splitting the aspects of Haiku-ka into borrowing topics and materials. In borrowing topics, the works with the topics of homesick and nostalgic parents stood out. Furthermore, annotations and understandings of Issa's original works were deeply involved. In borrowing materials, the original meanings in the works were transformed and changed or even reinterpreted by their own way. Eliminating sublime emotions, furious tone, and condemnation was main characteristic of Haiku-ka in Classic of Poetry(Shi jing). Besides, there were ways of exclusion of reasoning, deviating from the viewpoint of Sigyo(edification by poetry), not including moral senses. In other words, Issa used habits and impressions like the way of Haiku when he was doing Haiku-ka in Classic of Poetry(Shi jing). The meaning that Issa's Hiku-ka of Classic of Poetry(Shi jing) stood out compared to adaptation of Classic of Poetry(Shi jing) in Eastern Asia. Although Classic of Poetry(Shi jing) in Vietnam was transferred in the form of the poem in Vernacular, the meaning and contents were not changed. Moreover, the original works and characters in Joseon were not destroyed because Classic of Poetry(Shi jing) was not liberally translated but literally. However, Issa transferred the Classic of Poetry(Shi jing) in the form of the poem in Vernacular to reveal the value of popular ballads. This was a different adapting way of Classic of Poetry(Shi jing) from that in Eastern Asia.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.3
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• pp.313-331
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• 2022

Natural or native abiotic molecular hydrogen (H₂) is a major component in natural gas, however yet its importance in the global energy sector's usage as clean and renewable energy is underestimated. Here we review the occurrence and geological settings of native hydrogen to demonstrate the much widesprease H₂ occurrence in nature by comparison with previous estimations. Three main types of source rocks have been identified: (1) ultramafic rocks; (2) cratons comprising iron (Fe²⁺)-rich rocks; and (3) uranium-rich rocks. The rocks are closely associated with Precambrian crystalline basement and serpentinized ultramafic rocks from ophiolite and peridotite either at mid-ocean ridges or within continental margin(Zgonnik, 2020). Inorganic geological processes producing H₂ in the source rocks include (a) the reduction of water during the oxidation of Fe²⁺ in minerals (e.g., olivine), (b) water splitting due to radioactive decay, (c) degassing of magma at low pressure, and (d) the reaction of water with surface radicals during mechanical breaking (e.g., fault) of silicate rocks. Native hydrogen are found as a free gas (51%), fluid inclusions in various rock types (29%), and dissolved gas in underground water (20%) (Zgonnik, 2020). Although research on H₂ has not yet been carried out in Korea, the potential H₂ reservoirs in the Gyeongsang Basin are highly probable based on geological and geochemical characteristics including occurrence of ultramafic rocks, inter-bedded basaltic layers and iron-copper deposits within thick sedimentary basin and igneous activities at an active continental margin during the Permian-Paleogene. The native hydrogen is expected to be clean and renewable energy source in the near future. Therefore it is clear that the origin and exploration of the native hydrogen, not yet been revealed by an integrated studies of rock-fluid interaction studies, are a field of special interest, regardless of the presence of economic native hydrogen reservoirs in Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Sim-seong Yeon-gu
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• v.38 no.2
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• pp.269-304
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• 2023

The purpose of this paper is to present the psychological meaning of the creation myths by utilizing related myths, analysand's dreams, active imagination, and artwork to reveal the creative function of the unconscious. The creation myth is the phenomenon of projection when a new order is demanded in the chaotic phase of personal and human history. Depending on the attitude of the ego, it can be a sign of a reconstruction/alteration of consciousness or an invasion. Related literature such as Jung, von Franz, Neumann, Harding, and Edinger, domestic papers, and case reports are introduced to identify the background for this research. The psychological meaning of 'darkness' in creation myths is regarded as unconscious that is too dark to see. The Eskimo creation myth and an analysand's dreams of being blind and wandering in darkness are discussed in relation to nigredo in Alchemy. The psychological meaning of 'massa confusa' regards Uroboros, pleroma, early childhood experience, and a psychological womb in which everything is contained in one. With related myths and unconscious materials, a discussion is followed on how this realm can be a precursor of creation but also be trapped in an abyss. The psychological meaning of 'separation of world parent' is related to splitting one into two when unconscious contexts were touched before it became consciousness. Related myths, 'the world created between heaven and earth,' 'celestial being descending to the earth,' and 'the legend of relocation of a mountain,' as well as clinical material, are examined. Then this paper discusses the clinical implications of the separation of heaven and earth occurring on its own, that the creator's emotional aspects, such as loneliness and anxiety, are involved, and that delayed separation leads to the death of creatura and sudden separation leads to the death of the chaos. Then, the meaning of 'separation of world parent' is discussed in relation with separatio, the alchemical process of acquiring light/consciousness from darkness/unconsciousness. The psychological meaning of the creation of 'land/island' refers to the emergence of consciousness, the contents of the unconscious material into the realm of the ego. Related myths, such as the 'body of the monster/dragon becoming land' and analysand's dreams, are introduced, referring to the embodiment of Mercurius. This is followed by discussing related myths in creating the land to coagulatio in alchemy and utilizing creative work such as active imagination, art, music, and dance that can coagulate or concretize unconscious material in clinical approaches. Finally, myths of resurfaced land after the Flood or the complete destruction of the world in relation to the reconstruction of ego are discussed with related clinical material to show the importance of the analyst/therapist/supervisor's mental stability and capacity.

• Korean Journal of Mineralogy and Petrology
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• v.33 no.3
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• pp.273-291
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• 2020

The strength characteristics of the three orthogonal splitting planes, known as rift, grain and hardway planes in granite quarries, were examined. R, G and H specimens were obtained from the block samples of Jurassic granites in Geochang and Hapcheon areas. The directions of the long axes of these three specimens are perpendicular to each of the three planes. First, The chart, showing the scaling characteristics of three graphs related to the uniaxial compressive strengths of R, G and H specimens, were made. The graphs for the three specimens, along with the increase of strength, are arranged in the order of H < G < R. The angles of inclination of the graphs for the three specimens, suggesting the degree of uniformity of the texture within the specimen, were compared. The above angles for H specimens(θ^H, 24.0°~37.3°) are the lowest among the three specimens. Second, the scaling characteristics related to the three graphs of RG, GH and RH specimens, representing a combination of the mean compressive strengths of the two specimens, were derived. These three graphs, taking the various N-shaped forms, are arranged in the order of GH < RH < RG. Third, the correlation chart between the strength difference(Δσ_t) and the angle of inclination(θ) was made. The above two parameters show the correlation of the exponential function with an exponent(λ) of -0.003. In both granites, the angle of inclination(θ^RH) of the RH-graph is the lowest. Fourth, the six types of charts, showing the correlations among the three kinds of compressive strengths for the three specimens and the five parameters for the two sets of microcracks aligned parallel to the compressive load applied to each specimen, were made. From these charts for Geochang and Hapcheon granites, the mean value(0.877) of the correlation coefficients(R²) for total density(Lt), along with the frequency(N, 0.872) and density(ρ, 0.874), is the highest. In addition, the mean values(0.829) of correlation coefficients associated with the mean compressive strengths are more higher than the minimum(0.768) and maximum(0.804) compression strengths of three specimens. Fifth, the distributional characteristics of the Brazilian tensile strengths measured in directions parallel to the above two sets of microcracks in the three specimens from Geochang granite were derived. From the related chart, the three graphs for these tensile strengths corresponding to the R, G and H specimens show an order of H(R1+G1) < G(R2+H1) < R(R1+G1). The order of arrangement of the three graphs for the tensile strengths and that for the compressive strengths are mutually consistent. Therefore, the compressive strengths of the three specimens are proportional to the three types of tensile strengths. Sixth, the values of correlation coefficients, among the three tensile strengths corresponding to each cumulative number(N=1~10) from the above three graphs and the five parameters corresponding to each graph, were derived. The mean values of correlation coefficients for each parameter from the 10 correlation charts increase in the order of density(0.763) < total length(0.817) < frequency(0.839) < mean length(Lm, 0.901) ≤ median length(Lmed, 0.903). Seventh, the correlation charts among the compressive strengths and tensile strengths for the three specimens were made. The above correlation charts were divided into nine types based on the three kinds of compressive strengths and the five groups(A~E) of tensile strengths. From the related charts, as the tensile strength increases with the mean and maximum compressive strengths excluding the minimum compressive strength, the value of correlation coefficient increases rapidly.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.