• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.63-77
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• 2018

In the rapidly to change global market in recent years, as the era of merging and integrating industries and the evolution of technology have come to an era in which everything can not be solved as a single company, it is evolving into competition for the enterprise network rather than the competition for the enterprise unit. In a competitive business environment, it is necessary to provide not only for the efforts as an individual companies but also the mutual development efforts to enhance output through the innovation activities based on the interrelationship with the business partners. In spite of the recent efforts and research through core competencies and innovation activities, some of business activities were unable to achieve enough progress in business performance and this study mainly focused to improve business performance for those companies. This study targeted CEOs and Directors who participates in "manufacturing performance innovation partnership project" carried by The foundation of Large, SMEs, Agriculture, Fisheries cooperation Korea and studied the influences of innovation activities to the core competencies and business performance. Detailed variables in this study were extracted from the previous research and used for verification. The study is designed to determine the influence of individual innovation activities to the core competencies and business performance. Innovation activities as a parameter, the relationship between core competencies and business performance was examined. In the examination of the innovation activities as a meditated effect, those activities carried by SMEs (Collaboration in Technology, Manufacturing, and Management innovations with Large Scale Business) through partnership in manufacturing innovation is significantly related business performance. Therefore, the result reveals that the individual SMEs are having own limitation in the achievement of significant progress in business performance with their own capabilities, and using the innovation activities act as catalyst through the collaboration with large scale businesses would result significant progress in business performance. Mutual effort in collaborative innovation activities between large scale businesses and SMEs is one of the most critical issues in recent years in Korea and the main focus of this study is to provide analysis which demonstrates where the SMEs are required to focus in their innovation activities.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3159-3174
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• 1997

This paper reports a research to develop a methodology and a tool for understanding of very large and complex real-time software. The methodology and the tool mostly developed by the author are called the Architecture-based Real-time Software Understanding (ARSU) and the Software Re/reverse-engineering Environment (SRE) respectively. Due to size and complexity, it is commonly very hard to understand the software during reengineering process. However the research facilitates scalable re/reverse-engineering of such real-time software based on the architecture of the software in three-dimensional perspectives: structural, functional, and behavioral views. Firstly, the structural view reveals the overall architecture, specification (outline), and the algorithm (detail) views of the software, based on hierarchically organized parent-chi1d relationship. The basic building block of the architecture is a software Unit (SWU), generated by user-defined criteria. The architecture facilitates navigation of the software in top-down or bottom-up way. It captures the specification and algorithm views at different levels of abstraction. It also shows the functional and the behavioral information at these levels. Secondly, the functional view includes graphs of data/control flow, input/output, definition/use, variable/reference, etc. Each feature of the view contains different kind of functionality of the software. Thirdly, the behavioral view includes state diagrams, interleaved event lists, etc. This view shows the dynamic properties or the software at runtime. Beside these views, there are a number of other documents: capabilities, interfaces, comments, code, etc. One of the most powerful characteristics of this approach is the capability of abstracting and exploding these dimensional information in the architecture through navigation. These capabilities establish the foundation for scalable and modular understanding of the software. This approach allows engineers to extract reusable components from the software during reengineering process.

• Journal of Home Health Care Nursing
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• v.4
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• pp.5-22
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• 1997

The purpose of the study was to development an information delivery system for the home nursing service, to demonstrate and to evaluate the efficiency of it. The period of research conduct was from September 1996 to August 31, 1997. At the 1st stage to achieve the purpose, Firstly Assessment tool for the patients with cerebral vascular disease who have the first priority of HNS among the patients with various health problems at home was developed through literature review. Secondly, after identification of patient nursing problem by the home care nurse with the assessment tool, the patient's classification system developed by Park (1988) that was 128 nursing activities under 6 categories was used to identify the home care nurse's activities of the patient with CAV at home. The research team had several workshops with 5 clinical nurse experts to refine it. At last 110 nursing activities under 11 categories for the patients with CVA were derived. At the second stage, algorithms were developed to connect 110 nursing activities with the patient nursing problems identified by assessment tool. The computerizing process of the algorithms is as follows: These algorithms are realized with the computer program by use of the software engineering technique. The development is made by the prototyping method, which is the requirement analysis of the software specifications. The basic features of the usability, compatibility, adaptability and maintainability are taken into consideration. Particular emphasis is given to the efficient construction of the database. To enhance the database efficiency and to establish the structural cohesion, the data field is categorized with the weight of relevance to the particular disease. This approach permits the easy adaptability when numerous diseases are applied in the future. In paralleled with this, the expandability and maintainability is stressed through out the program development, which leads to the modular concept. However since the disease to be applied is increased in number as the project progress and since they are interrelated and coupled each other, the expand ability as well as maintainability should be considered with a big priority. Furthermore, since the system is to be synthesized with other medical systems in the future, these properties are very important. The prototype developed in this project is to be evaluated through the stage of system testing. There are various evaluation metrics such as cohesion, coupling and adaptability so on. But unfortunately, direct measurement of these metrics are very difficult, and accordingly, analytical and quantitative evaluations are almost impossible. Therefore, instead of the analytical evaluation, the experimental evaluation is to be applied through the test run by various users. This system testing will provide the viewpoint analysis of the user's level, and the detail and additional requirement specifications arising from user's real situation will be feedback into the system modeling. Also. the degree of freedom of the input and output will be improved, and the hardware limitation will be investigated. Upon the refining, the prototype system will be used as a design template. and will be used to develop the more extensive system. In detail. the relevant modules will be developed for the various diseases, and the module will be integrated by the macroscopic design process focusing on the inter modularity, generality of the database. and compatibility with other systems. The Home care Evaluation System is comprised of three main modules of : (1) General information on a patient, (2) General health status of a patient, and (3) Cerebrovascular disease patient. The general health status module has five sub modules of physical measurement, vitality, nursing, pharmaceutical description and emotional/cognition ability. The CVA patient module is divided into ten sub modules such as subjective sense, consciousness, memory and language pattern so on. The typical sub modules are described in appendix 3.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.47-50
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• 2009

Purpose: Presently, any exact standard of radiopharmaceutical doses in pediatric nuclear medicine doesn't exist in the universe. So hospitals are following by manual of vial kit or guidelines of America and Europe based on recommended adult doses adjusted for body mass (MBq/kg) or body surface area (MBq/$m^2$). However, especially for children younger than 1 year and heavier than 50 kg, it's hard to estimate exact dosage for those children. Materials and Methods: In order to obtain objective data of multipliers for pediatric studies, we surveyed 4 major hospitals in Korea. After receiving feedbacks, we changed dosage to multiplier. And we compared multipliers of Korea to America's and Europe's. Results: Most hospitals in Korea are following by body mass formula (MBq/kg). On the other hand, standards don't include proper factors for a child younger than 1 year and heavier than 50 kg. Multipliers for 3 kg children who are injected lower doses than needed are America:0.12, Europe:0.09, Korea:0.05, multipliers for 30 kg children who are injected proper doses are America:0.58, Europe:0.51, Korea:0.45 and multipliers for 60 kg children who are injected more doses than needed are America:0.95, Europe:0.95, Korea:0.91. Conclusions : Through the survey, when calculating doses for children, usually output doses are based on adult doses adjusted for body mass (MBq/kg) but research has shown that standards of all of the compared standards don't reflect exact multipliers for children younger than 1 year and heavier than 50 kg. Therefore, we should give an effort to reduce needless radiation exposure in children by establishing a proper doses standard and also developing better image reconstruction software.

• 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.

• Journal of Distribution Science
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• v.10 no.7
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• pp.33-41
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• 2012

The purpose of this study is to analyse the state of the energy industry and to determine the efficiency of its functioning on the basis of energy conservation principle and application of innovative technologies aimed at improving the ecological modernisation of agricultural sectors of Kazakhstan. The research methodology is based on an integrated approach of financial and economic evaluation of the effectiveness of the investment project, based on calculation of elasticity, total costs and profitability, as well as on comparative, graphical and system analysis. The current stage is characterised by widely spread restructuring processes of electric power industry in many countries through introduction of new technical installations of energy facilities and increased government regulation in order to enhance the competitive advantage of electricity market. Electric power industry features a considerable value of creating areas. For example, by providing scientific and technical progress, it crucially affects not only the development but also the territorial organisation of productive forces, first of all the industry. In modern life, more than 90% of electricity and heat is obtained by Kazakhstan's economy by consuming non-renewable energy resources: different types of coal, oil shale, oil, natural gas and peat. Therefore, it is significant to ensure energy security, as the country faces a rapid fall back to mono-gas structure of fuel and energy balance. However, energy resources in Kazakhstan are spread very unevenly. Its main supplies are concentrated in northern and central parts of the republic, and the majority of consumers of electrical power live in the southern and western areas of the country. However, energy plays an important role in the economy of industrial production and to a large extent determines the level of competitive advantage, which is a promising condition for implementation of energy-saving and environmentally friendly technologies. In these circumstances, issues of modernisation and reforms of this sector in Kazakhstan gain more and more importance, which can be seen in the example of economically sustainable solutions of a large local monopoly company, significant savings in capital investment and efficiency of implementation of an investment project. A major disadvantage of development of electricity distribution companies is the prevalence of very high moral and physical amortisation of equipment, reaching almost 70-80%, which significantly increases the operating costs. For example, while an investment of 12 billion tenge was planned in 2009 in this branch, in 2012 it is planned to invest more than 17 billion. Obviously, despite the absolute increase, the rate of investment is still quite low, as the total demand in this area is at least more than 250 billion tenge. In addition, industrial infrastructure, including the objects of Kazakhstan electric power industry, have a tangible adverse impact on the environment. Thus, since there is a large number of various power projects that are sources of electromagnetic radiation, the environment is deteriorated. Hence, there is a need to optimise the efficiency of the organisation and management of production activities of energy companies, to create and implement new technologies, to ensure safe production and provide solutions to various environmental aspects. These are key strategic factors to ensure success of the modern energy sector of Kazakhstan. The contribution of authors in developing the scope of this subject is explained by the fact that there was not enough research in the energy sector, especially in the view of ecological modernisation. This work differs from similar works in Kazakhstan in the way that the proposed method of investment project calculation takes into account the time factor, which compares the current and future value of profit from the implementation of innovative equipment that helps to bring it to actual practise. The feasibility of writing this article lies in the need of forming a public policy in the industrial sector, including optimising the structure of energy disbursing rate, which complies with the terms of future modernised development of the domestic energy sector.

• Journal of Korea Spatial Information System Society
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• v.3 no.2 s.6
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• pp.21-43
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• 2001

The environment to development information system including a GIS has been drastically changed in recent years in the perspectives of the complexity and diversity of the software, and the distributed processing and network computing, etc. This leads the paradigm of the software development to the CBD(Component Based Development) based object-oriented technology. As an effort to support these movements, OGC has released the abstract and implementation standards to enable approaching to the service for heterogeneous geographic information processing. It is also common trend in domestic field to develop the GIS application based on the component technology for municipal governments. Therefore, it is imperative to adopt the component technology considering current movements, yet related research works have not been made. This research is to propose a component-based GIS development methodology-ATOM(Advanced Technology Of Methodology)-and to verify its adoptability through the case study. ATOM can be used as a methodology to develop component itself and enterprise GIS supporting the whole procedure for the software development life cycle based on conventional reusable component. ATOM defines stepwise development process comprising activities and work units of each process. Also, it provides input and output, standardized items and specs for the documentation, detailed instructions for the easy understanding of the development methodology. The major characteristics of ATOM would be the component-based development methodology considering numerous features of the GIS domain to generate a component with a simple function, the smallest size, and the maximum reusability. The case study to validate the adoptability of the ATOM showed that it proves to be a efficient tool for generating a component providing relatively systematic and detailed guidelines for the component development. Therefore, ATOM would lead to the promotion of the quality and the productivity for developing application GIS software and eventually contribute to the automatic production of the GIS software, the our final goal.

• Korean Journal of Agricultural Science
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• v.35 no.1
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• pp.85-100
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• 2008

The United States Department of Agriculture (USDA) funded collaborative project on The Economic Feasibility Analysis of Crop Cultivation Practice Project in Pirganj and Kurigram Districts in Bangladesh will started during 2008-2012, for 4 years with total project cost of US$ 571,270. The project will be implemented in 6 villages; has 1,097 hectares areas which is divided into 948 hectares of agricultural land, 52 hectares of forest land and 345 hectares of other land, covered 1,059 households equal to 5,305 persons in Pirganj and Kurigram districts The project has proposed to be implemented in joint collaboration by Bangladesh Agricultural Research Council (BARC), Bangladesh Agricultural Research Institute (BARI) and Rangpur Dinajpur Rural Service (RDRS) Bangladesh with full participation of the farmers' groups of respective project site. The specific objectives of the project are: (1) to estimate the productivity of paddy, wheat, maize, tobacco and sugarcane (2) to determine the cost of production and returns to the above mentioned crops (3) to study the interrelationship between inputs and output of the above mentioned crops and (4) to examine the resource utilization patterns at farm level. In this project analysis, the net incremental profit is US$33,028. The expected incremental project benefit and incremented production cost are estimated as US$ 219,959 and US$ 186,931 respectively. The financial decision making criteria would be followed in this crop cultivation practice project. After the project implementation, the expected project benefits are assumed to be continued for 15 years. The benefit cost ratio (B/C) of the project is estimated at 1.077 (table 11) when using discount rate of 10% as an opportunity cost of capital in Bangladesh. FIRR of project is estimated at 26.15% which is bigger than the opportunity cost by more than double. So this project is financially feasible and acceptable. Therefore, this project should be extended to other areas to increase the farm income and economic growth of marginal poor farmers in Bangladesh.