• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Korean Journal of Soil Science and Fertilizer
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• v.38 no.2
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• pp.92-100
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• 2005

This study was conducted to find suitable methods for screening organic resources useful for compost. Twenty-seven industrial and domestic sludges were collected from various cities and industrial areas. Contents of organic matters in the sludges were in the range of 79.3-98.0%, and the contents were much higher than the regulation level (60%) for raw materials of compost. Contents of total nitrogen were in the range of 0.8-2.6%. Contents of Fe and Al were very high. Content of HEM was highest in textile sludge ($257mg\;kg^{-1}$) and the contents in the others were in the range of $12.6-90.3mg\;kg^{-1}$. Content of PAHs was lowest in food sludge ($739.1{\mu}g\;kg^{-1}$ and pulp-mill sludge had the highest PAHs content ($3461.8{\mu}g\;kg^{-1}$). $Microtox^{(R)}$ $EC_{50}$ values were higher in the sludges which were classified as a possible material in composting after analysis and investigation. Lettuce root elongation and $EC_{50}$ values were relatively lower in pulp-mill sludge, sewage sludge 3 (Large city), food sludge and leather sludge. Therefore, mineral nutrients, heavy metals, organic compounds (HEM, PAHs, PCBs), and bioassay ($Microtox^{(R)}$ $EC_{50}$, Relative root elongation test) are recommended to be included in the screening system of raw material of compost in addition to the current regulation with organic matter and 8 heavy metals.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• The Journal of the Petrological Society of Korea
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• v.28 no.1
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• pp.1-13
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• 2019

The characteristics of the rock cleavage of Jurassic Geochang granite were analysed using the parameters from the length and spacing-cumulative frequency diagrams. The evaluation for three planes and three rock cleavages was performed using the 25 parameters such as (1~2) slope angle(${\alpha}^{\circ}$and ${\beta}^{\circ}$), (3) intersection angle(${\alpha}-{\beta}^{\circ}$), (4) exponent difference(${\lambda}_S-{\lambda}_L$), (5~12) length of line(oa, ob, ol, os, ss', ll' and sl') and (13~15) length ratio(ol/os, ss'/ll' and ll'/sl'), (16) mean length((ss'+ll')/2), (17~23) area (${\Delta}oaa^{\prime}$, ${\Delta}obb^{\prime}$, ${\Delta}obb^{\prime}$, ${\Delta}oaa_a^{\prime}$, ${\Delta}obb_a^{\prime}$, ${\Delta}ll^{\prime}s^{\prime}$, ${\Delta}ss^{\prime}l^{\prime}$ and ⏢$ll^{\prime}ss^{\prime}$) and (24~25) area difference(${\Delta}obb^{\prime}-{\Delta}oaa^{\prime}$ and ${\Delta}obb_a^{\prime}-{\Delta}oaa_a^{\prime}$). Firstly, the values of the 11 parameters(group I: No. 1, 3~4, 7, 9~10, 13, 15~16, 20 and 25), the 3 parameters(group II: No. 5, 8 and 17) and the 2 parameters(group III: No. 12 and 22) are in orders of H(hardway) < G(grain) < R(rift), R < G < H and G < H < R, respectively. On the contrary, the values of parameters belonging to the above three groups show reverse orders for three planes. Secondly, the generalized chart for three planes and three rock cleavages were made. From the related chart, the distribution types formed by the two diagrams related to lengths and spacings were derived. The diagrams related to spacings show upward curvature in the chart of rift plane(G1 & H1, R') and hardway(H1 & H2, H). On the contrary, the diagrams related to lengths show downward curvature. These two diagrams take the form of a convex lens in the upper section. Besides, the two diagrams cross each other in the lower section. The overall shape formed by the above two diagrams between three planes($H^{\prime}{\rightarrow}G^{\prime}{\rightarrow}R^{\prime}$) and three rock cleavages($R{\rightarrow}G{\rightarrow}H$) display in reverse order. Lastly, these types of correlation analysis is useful for discriminating three quarrying planes.

• Korean Journal of Poultry Science
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• v.49 no.4
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• pp.189-197
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• 2022

This study was conducted to compare the egg productivity, egg quality, and blood characteristics of laying hens with different laying rates, and the frequency and cumulative duration of the sitting behavior observed before laying was investigated. Twelve 45-week-old Hy-Line Brown laying hens were randomly assigned to two treatment groups with three replicates. Treatment groups were classified as layers laying over 80%(high egg performance layers; HEP) and layers laying below 50%(poor egg performance layers; PEP). The experiment lasted 4 weeks. HEP showed higher hen-house egg production ratio and egg mass and lower feed conversion ratio(FCR) (P<0.05) compared with PEP, although egg weight was higher in PEP (P<0.05). In terms of egg quality, PEP showed differences in eggshell quality (eggshell color, eggshell thickness, and eggshell weight) (P<0.05). Additionally, HEP showed high triglycerides(TG), and PEP showed high alanine transaminase(ALT) level (P<0.05) in serum collected in the morning. In the afternoon, the HEP showed higher lactate dehydrogenase(LDH) levels (P<0.05). No differences in the Ca: P ratio were observed between layers with different laying rates. One hour before egg laying, HEP exhibited sitting behavior 4 times on average, each lasting 25 minutes. In conclusion, egg production and quality differ between HEP and PEP, and HEP showed frequent sitting behavior before egg laying. However, additional research is necessary to explore approaches other than specific behavioral observation to distinguish poor layers in the flock for application in farms.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.306-314
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• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• The Journal of the Petrological Society of Korea
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• v.25 no.2
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• pp.151-163
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• 2016

The characteristics of the rock cleavage in Jurassic granite from Geochang were analysed. The evaluation for the three directions of rock cleavages was performed using the parameters such as (1) frequency of microcrack spacing(N), (2) total spacing(${\leq}1mm$), (3) mean spacing($S_{mean}$), (4) difference value($S_{mean}-S_{median}$) between mean spacing($S_{mean}$) and median spacing($S_{median}$), (5) density of spacing(${\rho}$), (6) difference value between two exponents for the whole range of the diagrams(${\lambda}_H-{\lambda}_L$), (7) mean value of exponent(${\lambda}_M$), (8) mean value of exponential constant($a_M$), (9) difference value between two exponents for the section under the initial points of intersection(${\lambda}t_H-{\lambda}t_L$), (10) mean value of exponent(${\lambda}t_M$) and (11) mean value of exponential constant($at_M$). The results of correlation analysis between the values of parameters for three rock cleavages and those for three planes are as follows. The values of (I) parameters(1, 2, 7 and 8) and (II) parameters(3, 4 and 5) are in orders of (I) H(hardway, (H1 + H2)/2) < G(grain, (G1 + G2)/2) < R(rift, (R1 + R2)/2) and (II) R < G < H. On the contrary, the values of the above two groups(I~II) of parameters for three planes show reverse orders. Besides, the values of parameter $6({\lambda}_H-{\lambda}_L)$, parameter $9({\lambda}t_H-{\lambda}t_L)$, parameter $10({\lambda}t_M)$ and parameter $11(at_M)$ for three planes are in orders of R(rift plane, (G1 + H2)/2) < H(hardway plane, (R2 + G2)/2) < G(grain plane, (R1 + H2)/2), H < G < R, H < R < G and R < H < G, respectively. The values of the above four parameters for three rock cleavages show the various orders of R < H < G, R < H < G, H < G < R and H < G < R, respectively. Meanwhile, the spacing values equivalent to the initial points of contact and intersection between the two directions of diagrams were derived. The above spacing values for three rock cleavages are in order of rift(R1 and R2) < grain(G1 and G2) < hardway(H1 and H2). The spacing values for three planes are in order of rift plane(G1 and H1) < hardway plane(R2 and G2) < grain plane(R1 and H2). In particular, the intersection angles for three rock cleavages and three planes are in order of rift and rift plane < hardway and hardway plane < grain and grain plane. Consequently, the two diagrams of rift(R1 and R2) and rift plane(G1 and H1) show higher frequency of the point of contact and intersection. These characteristics of change were derived through the general chart for three planes and three rock cleavages. Lastly, the correlation analysis through the values of parameters along with the distribution pattern is useful for discriminating three quarrying planes.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Journal of Sasang Constitutional Medicine
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• v.11 no.2
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• pp.151-183
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• 1999

The object of this study was 200 patients who had been treated in the Oriental Medical Hospital at Dong Eui Medical Center during 9 months from Jan. 1999 to sept. 1999. We proceeded the judgment of Sasang Constitution according to 'Questionnaire of Sasang Constitution Classification (I)', and 'Questionnaire of Sasang Constitution Classification II(QSCCII)' and the diagnosis by a medical specialist. The following conclusion were made in comparison with Sasang Constitution and Questionnaire. 1. We selected the 84 subjects what had the statistical value out of the 196 subjects('Questionnaire of Sasang Constitution Classification (I)' had the 71 subjects and 'Questionnaire of Sasang Constitution Classification II(QSCCII)', had the 121 subjects). And we selected again the 73 subjects('Questionnaire of Sasang Constitution Classification (I)', had the 33 subjects and 'Questionnaire of Sasang Constitution Classification II (QSCC II)' had the 40 subjects) out of the 84 subjects, because it had a repeated subjects. 2. We made the Questionnaire what has the 85 subjects, including the subjects what was approved its statistical value by 'A CLINICAL STUDY OF THE JUDGMENT OF SASANG CONSTITUTION ACCORDING TO QUESTIONNAIRE' and 'A CLINICAL STUDY OF THE TYPE OF DISEASE AND SYMPTOM ACCORDING TO SASANG CONSTITUTION CLASSIFICATION'. The subject what ask the physique and the body form was 7, the subject what ask the external appearance and the posture was 7, the subject what ask the habit and the character was 3, the subject what ask the physiology and the pathology was 3, the subject what ask the phenomenon that he has frequency was 4, the subject what ask the eating was 3, the subject what ask the symptom that he has frequency was 14, the subject what ask the work and the qualities-defects was 6, the subject what ask the friendly intercourse was 7, the subject what ask the usual mind was 5, the subject what ask the emotional inclination was I, the subject what ask the behavioral inclination was 10, the subject what ask the character was 15. 3. In the new Questionnaire, the subject what has relevance to Soyang was 84, the subject what has relevance to Soeum was 87, the subject what has relevance to Taeeum was 70. And we made the point of subject with the statistical ratio. The total point of Soyang was 7785.04, the total point of Soeum was 7742.80, the total point of Taeeum was 7746.60. 4. As a result of judgment of Sasang Constitution between the clinical diagnosis by a medical specialist and the new Questionnaire, the diagnostic accuracy of new Questionnaire was 73.33%. The diagnostic accuracy of Soyang was low, the others was high. And the Taeyang was excepted.

• Korean Journal of Environmental Agriculture
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• v.23 no.4
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• pp.203-210
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• 2004

This study was conducted to assessment organic compound and bioassay (density of inhabited animal, fluctuation of predominant fungi, and survival ratio of earthworm) for finding damage on red pepper by heavily amount application of sludges in soil, which was treated with 3 pharmaceutical byproducts and a cosmetic industry wastewater sludge as raw materials of compost, and for establishing estimation method. HEM contents in the soil treated with pharmaceutical byproducts sludge2 (PS2) and cosmetic sludge (CS) were 0.51, 1.10 mg/kg respectively. PAHs content of PS2 treatment in the soil was 3406.8 ug/kg on July 8. In abundance of soil faunas, the pharmaceutical byproducts sludge2 treatment was the most highest. The next was decreased in the order of pig manure (PM) and the cosmetic sludge treatment. However the other pharmaceutical sludge treatments were remarkably reduced populations of soil inhabited animals. In upland soil treated with organic sludges, the numbers of bacteria and fungi of the pharmaceutical sludge treatment were 736, 909 cfu/g and those of the cosmetic sludge treatment were 440, 236 cfu/g, respectively. The pharmaceutical sludge treatments and the cosmetic sludge treatment in identification of predominant bacteria were not any tendency to compare with non fertilizer and pig manure treatments, but they had diverse bacteria than NPK treatment. In microcosm tests, the survival of the tiger earthworm in five soil samples was hardly affected against the soil of PSI (20%) after three months treated in the upland But after six months, survival of PS1 was 80%. At present, raw material of compost was authorized by contents of organic matter, heavy metal (8 elements), and product processing according to 'The specified gist on possible materials of using after analysis and investigation among raw materials of compost', however, for preparing to change regulation of raw material of compost and for considering to possibility of application, this study was conducted to investigate toxic organic compound and bioassay methods using inhabited animal, fungi, and earthworm without current regulation.