• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).

• 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.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Chest Surgery
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• v.36 no.3
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• pp.182-188
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• 2003

Background: Although ramicotomy (division of the rami communicantes of the thoracic sympathetic ganglia) is a selective and physiological surgical method for essential hyperhidrosis, it has some problems such as higher recurrence rates and the different surgical results among the patients and between left and right sides in the same individual. As one of the factors that are related to the differences in surgical result and recurrences, we investigated the anatomical variations of the rami communicantes. The purpose of this study is to help develop new surgical methods to decrease surgical differences among the patients or between the left and right sides of the same individual and recurrence rates in the clinical application of ramicotomy. Material and Method: We dissected 118 thoracic sympathetic chains in 59 adult Korean cadavers (male: 33, female: 26) to examine the anatomical variations of the rami communicantes from the second to the fourth thoracic sympathetic ganglia that have major components innervating to the hands. After the dissection of bilateral thoracic sympathetic chains, we compared the anatomy of left and right sides and examined the anatomical variations of rami communicantes. Result: The number and variation of communicating rami connecting the spinal nerves and the second sympathetic thoracic ganglion were much larger than lower levels. There was considerably less variability in the anatomy of the rami communicantes at successive levels. Among the 59 cadavers dissected, only 14.3% (9/59) had similar anatomy of thoracic sympathetic chains at both sides. As the components related to the essential palmar hyperhidrosis, intrathoracic nerve of Kuntz from the second thoracic sympathetic ganglion to the first intercostal nerve or brachial plexus were observed in 55.9% (66/118). The incidence of descending rami communicates from the second thoracic sympathetic ganglion to the third intercostal nerve and from the third thoracic sympathetic ganglion to the fourth intercostal nerve were 49.2% (58/118) and 28.0% (33/118). And the incidence of ascending rami communicates from the third thoracic sympathetic ganglion to the second intercostal nerve and from the fourth thoracic sympathetic ganglion to the third intercostal nerve were 6.8% (8/118) and 3.4% (4/118), respectively. Conclusion: Based on the various anatomical evidences of the rami communicantes from this study, only the ramicotomy at the third sympathetic ganglion level is insufficient for the treatment of the essential palmar hyperhidrosis to decrease the difference of surgical results and recurrences. When one is planning to perform the ramicotomy for the essential palmar hyperhidrosis, it is advantageous to divide the intrathoracic nerve of Kuntz on the second rib and the descending or ascending rami communicantes on the third and the fourth ribs as well as all the communicating rami from the third sympathetic ganglion.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Chest Surgery
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• v.41 no.1
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• pp.1-11
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• 2008

Background: The Damus-Kaye-Stansel (DKS) procedure is a proximal MPA-ascending aorta anastomosis used to relieve systemic ventricular outflow tract obstructions (SVOTO) and pulmonary hypertension. The purpose of this study was to review the indications and outcomes of the DKS procedure, including the DKS pathway and semilunar valve function. Material and Method: A retrospective review of 28 patients who underwent a DKS procedure between May 1994 and April 2006 was performed. The median age at operation was 5.3 months ($13\;days{\sim}38.1\;months$) and body weight was 5.0 kg ($2.9{\sim}13.5\;kg$). Preoperative pressure gradients were $25.3{\pm}15.7\;mmHg$ ($10{\sim}60\;mmHg$). Eighteen patients underwent a preliminary pulmonary artery banding as an initial palliation. Preoperative main diagnoses were double outlet right ventricle in 9 patients, double inlet left ventricle with ventriculoarterial discordance in 6,. another functional univentricular heart in 5, Criss-cross heart in 4, complete atrioventricular septal defect in 3, and hypoplastic left heart variant in 1. DKS techniques included end-to-side anastomosis with patch augmentation in 14 patients, classical end-to-side anastomosis in 6, Lamberti method (double-barrel) in 3, and others in 5. The bidirectional cavopulmonary shunt and Fontan procedure were concomitantly performed in 6 and 2 patients, respectively. Result: There were 4 hospital deaths (14.3%), and 3 late deaths (12.5%) with a follow-up duration of $62.7{\pm}38.9$ months ($3.3{\sim}128.1$ months). Kaplan-Meier estimated actuarial survival was $71.9%{\pm}9.3%$ at 10 years. Multivariate analysis showed right ventricle type single ventricle (hazard ratio=13.960, p=0.004) and the DKS procedure as initial operation (hazard ratio=6.767, p=0.042) as significant mortality risk factors. Four patients underwent staged biventricular repair and 13 received Fontan completion. No SVOTO was detected after the procedure by either cardiac catheterization or echocardiography except in one patient. There was no semiulnar valve regurgitation (>Gr II) or semilunar valve-related reoperation, but one patient (3.6%) who underwent classical end-to-side anastomosis needed reoperation for pulmonary artery stenosis caused by compression of the enlarged DKS pathway. The freedom from reoperation for the DKS pathway and semilunar valve was 87.5% at 10 years after operation. Conclusion: The DKS procedure can improve the management of SVOTO, and facilitate the selected patients who are high risk for biventricular repair just after birth to undergo successful staged biventricular repair. Preliminary pulmonary artery banding is a safe and effective procedure that improves the likelihood of successful DKS by decreasing pulmonary vascular resistance. The long-term outcome of the DKS procedure for semilunar valve function, DKS pathway, and relief of SVOTO is satisfactory.

• Journal of Sasang Constitutional Medicine
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• v.8 no.1
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• pp.101-186
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• 1996

The clinical application of constitutional Diagnosis is the most important part of Sasang constitutional medicine. It has been studied in various way. However, the study of morphologic characteristics on the face is applied for the first time. For quantitative analysis of the correlation between the sasang constitution and the shape of the face, the head-facial part of 170 cases were measured by Martin's measurement and analysis of a) the measurement value of height and the component ratio from the Gnathion to each part of face by constitution. b) the measurement value of depth and the component ratio from T-projected to each part of the face by constitution. c) the measurement value of breadth and component ratio between each parts of the facial breadth by constitution. d) the ratio of square on every part of face by constitution. e) the characteristics on each part of the face by constitution. f) the contour line of the forehead. g) the result of discriminant analysis about the constitution. Authors obtained the results from the study as follows; 1. The characteristics of Taeum-IN (1) The measurement value of Height, Breadth, T-Projected had a tendency to maximum value in general. (2) The value of lower opthal height and the square of lower opthal part was maximum. (3) The value of Pronasal T-projected length and Subnasal T-projected length was minimum, so Taeum-In has characteristics of depression in middle face, nasal part. (4) In the ratio of Breadth, T-Projected, T-Projected was minimum. (5) It was maximum that the square of nose, Alare, Middle face, Lower face and it was minimum that the square of eye. The square of nose, Alare, Middle facc, Lower face was maximum and the square of eye was minimum. (6) The curvature of the eyebrow was minimum. (7) The projection of jaw (Pogonion T-projection length) was maximum. (8) The breadth of eye was minimum. (9) There was a tendency that the projection of the forehead to the right in general. 2. The characteristics of Soeum-In (1) In all cases of projected length the measurement value was minimum. (2) The value of lower opthal height and the square of lower opthal part was minimum. (3) By the Pupulare T-projected length, the value of Pronasal T-projected length and Subnasal T-projected length was minimum, so the Soeum In's face shape is flat. (4) The square of eye, mouth, forehead was maximum and the square of nose, Alare, Middle face, Lower face was minimum. (5) The curvature of the eyebrow was maximum. (6) The projection of mouth was minimum. (7) The jaw was flat. (8) The breadth of eye was maximum. (9) There was a tendency that the projection of the forehead to the left in general. 3. The characteristics of Soyang-In. (1) In most cases of 고경 length the measurement value was minimum. (2) By the Pupulare T-projected length, each ratio of projected length was maximum, so the Soyang-In's face shape has many protrusions (3) In the ratio of Breadth, T-Projected, T-Projected was maximum. (4) The square of mouth was minimum. (5) The inclination of the forehead was minimum. (6) The projection of mouth was maximum. (7) The breadth of eye was minimum. (8) There was a tendency that the projection of the forehead to the left in general. (9) The middle face was protruded. 4. Discriminant about the constitution. According to the result of discriminant, the accuracy probability of discriminant was 85.58% in total and Taeum-In was 90.5%, Soeum-In was 70.8%, Soyang-In was 89.5%. The accuracy probability of discriminant about 3 constitutional group increased by 49.03% than the accident probility 36.55% 5. Suggestion (1) The study which gather and analysis the data should be continued. (2) The study which subdivide the characteristics of each part of the face by the constitution should be continued. (3) The analysis method about Moire should be supplement. (4) The study about the morphologic characteristics of the whole body should be continued. (5) Computer program of constitution diagnosis should be developed. (6) To increase utility of this method, the measurement should be automation.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Journal of Korean Physical Therapy
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• v.16 no.1
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• pp.85-123
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• 2004

Back ground : Subcutaneous fat area is the main factor involved in replacement disease and arteriosclerosis. Simple weight control is the appropriate medical treatment. It's understood that weight reduction does not only reduce the fat concentrations in blood but also reduces blood pressure, improves glucose levels in diabetes patients and reduces incidents of heart disease. there are several methods for reducing fat in the abdominal region but their effectiveness is not folly understood. one method is electrical stimulation of the problem areas. Method : From May 1st 2002 to October 31st. The 15 subjects who received medical examination were aged between 25 and 53 and were of mixed gender. The subjects were divided into two groups one to received functional electrical stimulation and the other a control group. Using Broca's criterion for judging fat grades. I analysed the differences between the two groups before and after the treatment. Subjects received functional electrical stimulation on the abdominal muscle intensity 50Hz. They received this treatment 4 days a week for 40 minutes a day. In the case of aerobic exercise, at the Treadmill, we used it with the intensity of $75\%$ maximum heart rate (220-age). Result 1)After functional electrical stimulation in the case of male subjects, the weight was reduced 1.93kg, obesity $2.60\%$, fat mass 2.73kg, Percent body fat $4.40\%$, waist circumference 6.53cm, circumference of hips 5.53cm. On the other side, the quality of muscle was increased at the rate of 1.03kg, but it's not attentional level. The subcutaneous fat area was reduced by $26.63cm^2$, the visceral fat area was reduced by $43.00cm^2$, In the female subjects, we can see the reduction of fat grade by $26.63cm^2$, the quantity of body fat by 1.5kg, percent body fat by $1.77\%$, circumference of waist by 4.02cm, circumference of hips by 3.67cm, weight by 1.40kg but was increased 0.72kg at the quantity of muscles. We can see the reduction also in the subcutaneous fat area $24.03cm^2$, the visceral fat area by $25.36cm^2$. 2)After aerobic exercise, on the male subjects, we can see reduction of weight by 3.36kg, obesity by $4.00\%$, fat mass by 2.83kg and we can see increase at the soft lean mass by 2.96kg, but we can see reduction, the percent body fat by $3.03\%$, fat distribution by $0.023\%$, circumference of waist by 3.10cm, circumference of hips by 2.23cm. The female subjects show a reduction in the weight by 2.48kg, percent body fat by $2.20\%$, show an increase in the soft lean mass by 1.54kg. We can see a reduction in the quantity of fat mass by 2.32kg, the percent body fat by $2.80\%$, the circumference of waist by 2.16cm, the circumference of hips by 2.68cm, the fat distribution by $0.016\%$, the subcutaneous fat area by $15.25cm^2$ the visceral fat area by $11.52cm^2$. After aerobic exercise, we can't see the attentional change at the total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol. 3)After the application of functional electrical stimulation and aerobic exercise, in result of measurement on the body ingredient, we could see the weight reduction and increase the quantity of muscle with the male group who exercised aerobic. We can see the attentional rate on the electrical stimulation about abdominal fat rate, circumference of waist, circumference of hips. The other hand, I couldn't see the attentional differences between the two groups in the rate of fatness and quantity of body fat and the rate of body fat. There isn't any attentional difference in the area of fat under skin, on the contrary, There is attentional difference in the fat in the internal organs area at the electrical stimulation site. We can't see the attentional change of total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol between electrical stimulation and aerobic exercise. 4)After execution of functional electrical stimulation and aerobic exercise, in result of measurement on change of body ingredient among female objects, We could see weight reduction, increase at muscle quantity in the aerobic exercise group. We could see the attentional differences in the rate of fatness, the rate of abdominal region, the circumference which received electrical stimulation. But, we couldn't see the attentional differences between two groups in the quantity of body fatness, the circumference of hips. The subcutaneous fat area doesn't show the attentional differences. On the Contrary, we could see lots of differences in the visceral fat area of the electrical stimulation group. Conclusion The results show that functional electrical stimulation and aerobic exercise have insignificant differences when if comes to total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol. Though there is affirmative change in body ingredient after both electrical stimulation and aerobic exercise. Functional electrical stimulation is more effective on the subcutaneous fat area and in changing visceral fat area. There fore. It is concluded that the physical therapy is more effective in the treatment of abdominal fatness.