• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.8
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• pp.1220-1230
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• 2010

The purposes of this study were to compare price sensitivity analysis for using environmental-friendly agricultural products in university foodservice between Jeonnam and Gyeongnam areas in Korea and to suggest the optimum guideline for price increase. The questionnaires were distributed to 600 university students respectively in Jeonnam and Gyeongnam area from July 15 to July 25, 2008; among them, 570 students from Jeonnam area and 490 students from Gyeongnam area responded. The results of this study were as follows. First, Indifference price (IDP) were 890 won (Jeonnam area) and 1,050 won (Gyeongnam area); Optimum price point (OPP) were 1,030 won (Jeonnam area) and 1450 won (Gyeongnam area). Price stress range were 140 won (890~1030 won) in Jeonnam area and 400 won (1050~1450 won) in Gyeongnam area. Second, point of marginal cheapness (PMC) were 500 won (Jeonnam area) and 790 won (Gyeongnam area) whereas point of marginal expensiveness (PME) were 1,170 won (Jeonnam area) and 1820 won (Gyeongnam area). Range of acceptable price (RAP) were 670 won (500~1170 won) in Jeonnam area and 1030 won (790~1820 won) in Gyeongnam area. Third, on the basis of IDP percentage and RAP, students in Jeonnam area were more sensitive to meal price increase than students in Gyeongnam area. In contrast, on the basis of Price Stress, students in Gyeongnam area were more sensitive to meal price increase than students in Jeonnam area. Hence, when using environmental-friendly agricultural products in university foodservice, in Jeonnam area, meal price increase should be recommended to be in RAP (500~1170 won), and in Gyeongnam area, meal price increase should be recommended to be in RAP (790~1820 won).

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.5
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• pp.631-640
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• 2014

The inhibitory effects of Boswellia serrata (BW) extracts on degenerative osteoarthritis were investigated in primary-cultured rat cartilage cells and a monosodium-iodoacetate (MIA)-induced osteoarthritis rat model. To identify the protective effects of BW extract against $H_2O_2$ ($800{\mu}M$, 2 hr) in vitro, cell survival was measured by MTT assay. Cell survival after $H_2O_2$ treatment was elevated by BW extract at a concentration of $20{\mu}g/mL$. In addition, BW extract treatment significantly reduced and normalized the productions of pro-inflammatory factors, nuclear transcription factor ${\kappa}B$, cyclooxygenase-2, tumor necrosis factor-${\alpha}$, and interleukin-6 at a concentration of $20{\mu}g/mL$. Treatment of chondrocytes with BW extract significantly reduced 5-lipoxygenase activity and production of prostaglandin E2, especially at a concentration of $10{\sim}20{\mu}g/mL$. For the in vivo animal study, osteoarthritis was induced by intra-articular injection of MIA into knee joints of rats. Consumption of a diet containing BW extract (100 and 200 mg/kg) for 35 days significantly inhibited the development and severity of osteoarthritis in rats. To determine the genetic expression of arthritic factors in articular cartilage, real-time PCR was applied to measure matrix metalloproteinases (MMP-3, MMP-9, and MMP-13), collagen type I, collagen type II, and aggrecan, and BW extract had protective effects at a concentration of 200 mg/kg. In conclusion, BW extract was able to inhibit articular cartilage degeneration by preventing extracellular matrix degradation and chondrocyte injury. One can consider that BW extract may be a potential therapeutic treatment for degenerative osteoarthritis.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.8
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• pp.1303-1317
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• 2013

The purpose of this study was to determine the sodium (Na) and potassium (K) content of school meals served in elementary and junior high school in Korea. In this study, 872 kinds of school meal dishes were collected from twelve elementary and twelve junior high schools located in four different cities in Korea (Daegu, Masan, Gwangju, and Jeju). The dishes were classified into three main categories; staple dish, subsidiary dish, and dessert. Each main category was further sub-classified into 4 kinds of staple dishes, 15 kinds of subsidiary dishes, and 5 kinds of dessert dishes. The Na and K content of dishes were then analyzed by atomic absorption spectroscopy. The Na content of individual dishes showed considerable differences, ranging from 9 to 2,717 mg/100 g. Among the staple dishes, cooked rice contained relatively less Na, but other staple dishes such as a la carte, noodle, and rice-gruel contained considerably high amounts of Na. Regarding the subsidiary dishes, the Na content of salad was low, but those of Jangachi, stir-fried dishes, and kimchi were considerably high. Among the dessert dishes, beverages, fruit, and milk/dairy products contained relatively low amount of Na, while rice cakes and baked goods, and snacks contained noticeably high amounts of Na. Unlike the Na content, the K content between the dishes did not show much variability. Cooked rice and rice cakes contained relatively low amounts of K, similar to other dishes, and ranged from 104 to 220 mg/100 g. The Na/K ratio was especially high in rice cakes and Jangachi, while of the ratio in beverages, milk/dairy products, salad, and fruit were pretty low. The total content of Na and K and the Na/K ratio of elementary school meals were 974 mg, 378 mg and 2.7, respectively, and those in junior high school meals was 1,466 mg, 528 mg and 3.0. The results show that most school meals provide a significant amount of Na but significantly small amounts of K, as suggested by the Dietary Reference Intakes for Koreans.

• Journal of Yeungnam Medical Science
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• v.8 no.2
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• pp.185-201
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• 1991

A household survey was conducted to compare the patterns of morbidity and medical care utilization between medical aid beneficiaries and medical insurance beneficiaries. The study population included 285 medical aid beneficiaries that were completely surveyed and 386 medical insurance benficiaries selected by simple random sampling from a Dong(Township) in Taegu. Well-trained surveyers mainly interviewed housewives with a structured questionnaire. The morbidity rates of acute illness during the 15-day period, were 63 per 1,000 medical aid beneficiaries and 62 per 1,000 medical insurance beneficiaries. The rates for chronic illness were 123 per 1,000 medical aid beneficiaries and 73 per 1,000 medical insurance beneficiaries. The most common type of acute illness in medical aid and medical insurance beneficiaries was respiratory disease. In medical aid beneficiaries, musculoskeletal disease was most common, but in medical insurance beneficiaries, gastrointestinal disease was most common. The mean duration of acute illness of medical aid beneficiaries was 3.8 days and that of medical insurance beneficiaries was 6.8 days. During the one year period, mean duration of medical aid beneficiaries chronic illnesses was 11.5 months which was almost twice as long compared to medical insurance beneficiaries. Pharmacy was most preferrable facility among the acute illness patient in medical aid beneficiaries, but acute cases of medical insurance beneficiaries visited the clinic most commonly. Chronic cases of both groups visited the clinic most frequently. There were some findings suggesting that much unmet need existed among the medical aid beneficiaries. In acute cases, the average number of days of medical aid users utilized medical facilities was less than medical insurance users. On the other hand, the length of medical care utilization of chronic cases was reversed. Geographical accessibility was the most important factors in utilization of medical facilities. Almost half of the study population answered the questions about source of funds on medical security correctly. Most respondents considered that the objective of medical security was afford ability. The chief complaint on hospital utilization was the complicated administrative procedures. These findings suggest that there were some problems in the medical aid system, especially in the referral system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.1 no.1
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• pp.53-68
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• 1981

Most clays under sustained load exhibit time-dependent deformation because of creep movement of soil particles and many investigators have attempted to relate their findings to the creep behavior of natural ground and to the long-term stability of slopes. Since the creep behavior of clays may assume a variety of forms depending on such factors as soil plasticity, activity and water content, it is difficult and complicated to analyse the creep behavior of clays. Rheological models composed of linear springs in combination with linear or nonlinear dashpots and sliders, are generally used for the mathematical description of the time-dependent behavior of soils. Most rheological models, however, have been proposed to simulate the behavior of secondary compression for saturated clays and few definitive data exist that can evaluate the behavior of non-saturated clays under the action of sustained stress. The clays change gradually from a solid state through plastic state to a liquid state with increasing water content, therefore, the rheological models also change. On the other hand, creep is time-dependent, and also the effect of thixotropy is time-function. Consequently, there may be certain correlations between creep behavior and the effects of thixotropy in compacted clays. In addition, the states of clay depend on water content and hence the height of the specimen under drained conditions. Futhermore, based on present and past studies, because immediate elastic deformation occurs instantly after the pressure increment without time-delayed behavior, the factor representing immediate elastic deformations in the rheological model is necessary. The investigation described in this paper, based on rheological model, is designed to identify the immediate elastic deformations and the effects of thixotropy and height of clay specimens with varing water content and stress level on creep deformations. For these purposes, the uniaxial drain-type creep tests were performed. Test results and data for three compacted clays have shown that a linear top spring is needed to account for immediate elastic deformations in the rheological model, and at lower water content below the visco-plastic limit, the effects of thixotropy and height of clay specimens can be represented by the proposed rheological model not considering the effects. Therefore, the rheological model does not necessitate the other factors representing these effects. On the other hand, at water content higher than the visco-plastic limit, although the state behavior of clays is visco-plastic or viscous flow at the beginning of the test, the state behavior, in the case of the lower height sample, does not represent the same behavior during the process of the test, because of rapid drainage. In these cases, the rheological model does not coincide with the model in the case of the higher specimens.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of Distribution Research
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• v.16 no.2
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• pp.1-24
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• 2011

1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in