• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Education of Primary School Mathematics
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• v.25 no.4
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• pp.395-410
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• 2022

Nominalization is one of the grammatical metaphors, and it is the representation of verbal meaning through noun equivalent phrases. In mathematical word problems, texts using nominalization have both the advantage of clarifying the object to be noted in the mathematization stage, and the disadvantage of complicating sentence structure, making it difficult to understand the sentences and hindering the experience of the full steps in mathematical modelling. The purpose of this study is to analyze word problems in the textbooks from the perspective of nominalization, a linguistic element, and to derive implications in relation to students' difficulties during solving the word problems. To this end, the types of nominalization of 341 word problems from the content domain of 'Numbers and Operations' of elementary math textbooks according to the 2015 revised national curriculum were analyzed in four aspects: grade-band group, main class and unit assessment, specialized class, and mathematical expression required word problems. Based on the analysis results, didactical implications related to the linguistic expression of the mathematical word problems were derived.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Korean Institute of Interior Design Journal
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• v.13 no.1
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• pp.3-10
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• 2004

The subject on presentation technique is the class which helps to develop the capability of sensual and creative acts to the students who major in interior design. It helps to understand plasticity and space through the theory of the light and the object, and to develop the capability which realizes one's idea to the actual and visual formations. It also enable the students to explain their designs to the customers by studying the technique which transfers 2D diagram to 3D presentation. Because of these objectives of the class, the departments which major in interior design in Korea consider the class on presentation technique as one of the mun subjects in basic curriculum. However, most of the textbooks related to the presentation technique published in Korea are in the lack of original characteristics for the field of domestic interior design since they refer to or employ the contents of architectural field by directly translating foreign articles. In addition, most of the classes on the presentation technique are taught not by professors but by lecturers so that the class objectives may be varied by their standards for the class. Therefore, it is not easy for each department to set up the one's own characteristics in the field of presentation technique. This study is to analyze the problems in the subjects of presentation technique by investigating the current situations for the subjects on presentation technique in domestic colleges and by surveying the personnels engaged in the field of domestic interior design. Also, the study has the objective to propose the development plan of educational program for the presentation technique in order to enable the student to effectively work in real business.

• Journal of Environmental Science International
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• v.1 no.1
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• pp.35-43
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• 1992

Handling the emergency problems such as Chemobyl accident require real time prediction of pollutants dispersion. One-point real time sounding at pollutant source and simple model including turbulent-radiation process are very important to predict dispersion at real time. The stability categories obtained by one-dimensional numerical model (including PBL dynamics and radiative process) are good agreement with observational data (Golder, 1972). Therefore, the meteorological parameters (thermal, moisture and momentum fluxes; sensible and latent heat; Monin-Obukhov length and bulk Richardson number; vertical diffusion coefficient and TKE; mixing height) calculated by this model will be useful to understand the structure of stable boundary layer and to handling the emergency problems such as dangerous gasses accident. Especially, this simple model has strong merit for practical dispersion models which require turbulence process but does not takes long time to real predictions. According to the results of this model, the urban area has stronger vertical dispersion and weaker horizontal dispersion than rural area during daytime in summer season. The maximum stability class of urban area and rural area are "A" and "B" at 14 LST, respectively. After 20 LST, both urban and rural area have weak vertical dispersion, but they have strong horizontal dispersion. Generally, the urban area have larger radius of horizontal dispersion than rural area. Considering the resolution and time consuming problems of three dimensional grid model, one-dimensional model with one-point real sounding have strong merit for practical dispersion model.al dispersion model.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.267-277
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• 2021

Both teachers and learners should participate in non-face-to-face class due to COVID-19. The non-face-to-face class has brought about many problems, where they made adequate preparations for such abrupt situation. This study attempted to understand and improve problems occurring during non-face-to-face midi class. The findings are as follows: First, there were differences in equipment available to contact and non-face-to-face class. Such a problem could be improved by using Reaper, DAW which can be installed and freely utilized without any functional limits, regardless of the types of operating systems. Second, latency could not be reduced, when the screen share function of Zoom was used, since it was impossible to select audio interface's drivers in DAW. This problem was improved by again receiving audio output as input and sending it, from the perspectives of teachers. In addition, learners who used the operating system of Windows and have no audio interfaces usually suffer from latency during practices. The latency can be reduced by installing Asio4all. Third, image degradation and screen disconnection phenomena occurred due to the lack of resource. Two computers were connected by using a capture board and the screen disconnection phenomena could be improved by distributing resources and maintaining high-resolution. The system for allowing non-face-to-face midi class could be successfully established, as one more computer was connected by using Vienna Ensemble Pro and more plug-ins were used by securing additional resources. Consequently, the problems of non-face-to-face midi class could be understood and improved.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.355-361
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• 1999

An application of case-based reasoning is proposed to build an influence diagram for identifying successful new ventures. The decision to invest in new ventures in characterized by incomplete information and uncertainty, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Influence diagrams are used as a model for representing investment decision problems based on incomplete and uncertain information from a variety of sources. The building of influence diagrams needs much time and efforts and the resulting model such as a decision model is applicable to only one specific problem. However, some prior knowledge from the experience to build decision model can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem solving experience to solve a new decision. In this paper, we suggest a case-based reasoning approach to build an influence diagram for the class of investment decision problems. This is composed of a retrieval procedure and an adaptation procedure. The retrieval procedure use two suggested measures, the fitting ratio and the garbage ratio. An adaptation procedure is based on a decision-analytic knowledge and decision participants knowledge. Each step of procedure is explained step by step, and it is applied to the investment decision problem in new ventures.

• Korean Management Science Review
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• v.17 no.2
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• pp.75-86
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• 2000

In evolutionary algorithm, there exist various models for the evolution of the population with respect to schemes and strategies for reproduction. In the application of the algorithm to a specific problem, one model suitable to the problem is to be properly chosen and a program expert or a software is needed to help implement and test a designed algorithm. In this study, abject oriented modeling and the class library for simple evolutionary algorithms(SEA) with one population is developed. The library proposed here can be used as a generalized tool for solving problems in a wide range of domains.

• Bulletin of the Korean Mathematical Society
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• v.55 no.3
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• pp.999-1006
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• 2018

In this article we consider the class ${\mathcal{A}}(p)$ which consists of functions that are meromorphic in the unit disc $\mathbb{D}$ having a simple pole at $z=p{\in}(0,1)$ with the normalization $f(0)=0=f^{\prime}(0)-1$. First we prove some sufficient conditions for univalence of such functions in $\mathbb{D}$. One of these conditions enable us to consider the class ${\mathcal{A}}_p({\lambda})$ that consists of functions satisfying certain differential inequality which forces univalence of such functions. Next we establish that ${\mathcal{U}}_p({\lambda}){\subsetneq}{\mathcal{A}}_p({\lambda})$, where ${\mathcal{U}}_p({\lambda})$ was introduced and studied in [2]. Finally, we discuss some coefficient problems for ${\mathcal{A}}_p({\lambda})$ and end the article with a coefficient conjecture.

• Journal of the Korean Society of Earth Science Education
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• v.5 no.1
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• pp.75-87
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• 2012

The purpose of this study is to find out the effects of internet resource-based problem-based learning on the academic achievement in science and the attitude toward science of elementary school students. One experiment class and one control class of grade 6 students were selected to perform a prior investigation on the academic achievement in science and the attitude toward science, then the experiment class attended 4 weeks of lessons that was applied the internet resource-based problem-based learning, and the control class attended the traditional lessons based on the guidelines of teachers. After conducting lessons, a post investigation was performed for each class and the results were analyzed to produce the following conclusions. First, the internet resource-based problem-based learning could be seen to be effective in improving the students' academic achievements in science. The internet resource-based problem-based learning seemed to make students recognize the lesson details better and grasp well the questions given during lessons from the process of finding solutions among many informations and data on the internet. Second, the internet resource-based problem-based learning had a positive effect on all attitudes' areas toward science of students. It looked like that the internet resource-based problem-based learning taught the students to use the internet resources and gave them a friendly feeling, so the children could actively participate in class and had positive recognition on science. Third, from teacher observation and the result of the student recognition investigation, we could know that the students showed lots of interests in the internet resource-based problem-based learning, and they were able to understand the scientific theories in the process of solving problems that were relevant to real life, and thought science in a positive way.