• Journal of the korean academy of Pediatric Dentistry
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• v.25 no.1
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• pp.162-170
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• 1998

The purpose of this study is to provide framework for understanding children's dental utilization. In this paper Andersen-Newman's model is applied to the use of dental visits. This model consists of predisposing, enabling, and need components that describe a person's decision to use dental health services. One thousand, nine hundred seven children and their mothers were selected for the study. The children were fourth grade to sixth grade in elementary schools in Iksan city, Korea. Models are operationalized using stepwise multiple regression analysis and path analysis. The number of independent variables used in the analysis was 39 in total, ie 32 predisposing components, 6 enabling components, and 1 need component. Children's Dental utilization was measured based on the number of visits. The data collected by means of a questionnaire survey. In this study, the amount of variance by the model was 25 percent. Predisposing factors had the greatest effect on utilization. Number of restricted activity days caused by oral disease, having a regular dental care, and susceptibility on oral disease of children were found to have significant major effects on dental utilization of children. Mother's dental visits was most important factor affecting dental utilization of children.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.281-293
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• 2020

Recently, large and small fires have been happening more often in Korea. Fire is one of the most frequent disasters along with traffic accidents in korean cities, and this frequency is closely related to the land use and the type of facilities. Therefore, in this study, the significance of fires was analyzed by considering land use, facility types, human and social factors and using 10 years of fire data in Jinju city. Based on this, OLS (Ordinary Least Square) regression analysis, SLM (Spatial Lag Model) and SEM (Spatial Error Model) using space weights, were compared and analyzed considering the location of the fire and each factor, then a statistical model with high suitability was presented. As a result, LISA analysis of spatial distribution patterns of fires in Jinju city was conducted, and it was proved that the frequency of fires was high in the order as follow, central commercial area, industrial area and residential area. Multiple regression analysis was performed by integrating demographic, social, and physical variables. Therefore, the three models were compared and analyzed by applying spatial weighting to the derived factors. As a result of the significance test, the spatial error model was analyzed to be the most significant. The facilities that have the highest correlation with fire occurrence were second type neighborhood facilities, followed by detached house, first type neighborhood facilities, number of households, and sales facilities. The results of this study are expected to be used as significant data to identify factors and manage fire safety in urban areas. Also, through the analysis of the standard deviation ellipsoid, the distribution characteristics of each facility in the residential area, industrial area, and central commercial area among the use areas were analyzed. In, the second type neighborhood facility with the highest fire risk was concentrated in the center. The results of these studies are expected to be used as useful data for identifying factors and managing fire safety in urban areas.

• Korean Security Journal
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• no.29
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• pp.139-162
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• 2011

The purpose of this study is to compare with the general and behavioral characteristics between simple and serial arsonists using the data derived from Scientific Crime Analysis System, Criminal Filing Search System, and Crime Information Management System. The analysis and findings reported here are derived from data extracted from 160 arsonists arrested by police officer. The independent variables included such socio-economic characteristic as arsonists' gender, age, occupation, education level, and previous criminal records of arsonists, and finally the general characteristics of the scene of fire settings. The dependent variable is whether or not serial fire setter. To achieve the purpose, the analysis of frequencies and cross-tab were conducted. According to frequence and cross-tab analysis, there are great differences of the general and behavior characteristics between two groups. In the comparison of simple and serial arsonists, serial arsonists are more likely to have previous criminal records, low socio-economic status, unmarried and no cohabitants than simple arsonists. furthermore, serial arsonists are more likely to use garbage papers for fire setting in the scene of the crime, to have mental or psychological problems, and to get involved in fire setting for the psychological pleasure than simple arsonists do. The present research has some obvious limitations. First, the analysis is based only on arsonists arrested by police officers. These may be considerable differences in arsonists arrested by police officers and fire setters not arrested by them. Additional research is needed to assess the extent to which these findings would apply to fire setters not arrested by police officer in Korea. Secondly, the data in this study are cross-sectional and simple cross-tab analysis are used. Potential limitation of cross-sectional data concerns the inability to specify the changes in measures as arsonists behavioral characteristics. Therefore, further studies need to use longitudinal data and more complicate statistical techniques such as correlation analysis, multiple regression analysis, or LISREL models to specify the casual relationships between dependent and independent variables for fire settings. Even if this study has some limitations, it is meaningful in which it first investigated the comparison of simple and serial arsonists focusing on the general and behavioral characteristics between two groups in Korea.

• 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.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.194-201
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• 2021

Induced polarization (IP) effect is known to be caused by electrochemical phenomena at interface between minerals and pore water. Spectral induced polarization (SIP) method is an electrical survey to localize subsurface IP anomalies while injecting alternating currents of multiple frequencies into the ground. This method was effectively applied to mineral exploration of various ore deposits. Titanomagnetite ores were being produced by a mining company located in Gonamsan area, Gwanin-myeon, Pocheon-si, Gyeonggi-do, South Korea. Because the ores contain more than 0.4 w% vanadium, the ore deposit is called as Gwanin vanadiferous titanomagnetite (VTM) deposit. The vanadium is the most important of materials in production of vanadium redox flow batteries, which can be appropriately used for large-scale energy storage system. Systematic mineral exploration was conducted to identify presence of hidden VTM orebodies and estimate their potential resources. In geophysical exploration, laboratory geophysical measurement of rock samples is helpful to generate reliable property models from field survey data. Therefore, we performed laboratory SIP data of the rocks from the Gwanin VTM deposit to understand SIP characteristics between ores and host rocks and then demonstrate the applicability of this method for the mineral exploration. Both phase and resistivity spectra of the ores sampled from underground outcrop and drilling cores were different of those of the host rocks consisting of monzodiorite and quartz monzodiorite. Because the phase and resistivity at frequencies below 100 Hz are mainly dependent on the SIP characteristics of the rocks, we calculated mean values of the ores and the host rocks. The average phase values at 0.1 Hz were ores: -369 mrad and host rocks: -39 mrad. The average resistivity values at 0.1 Hz were ores: 16 Ωm and host rocks: 2,623 Ωm. Because the SIP characteristics of the ores were different of those of the host rocks, we considered that the SIP survey is effective for the mineral exploration in vanadiferous titanomagnetite deposits and the SIP characteristics are useful for interpreting field survey data.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Global Scholars of Marketing Science
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• v.20 no.3
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• pp.239-248
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• 2010

This paper describes a collaborative project between academia and industry which focused on improving the marketing and product development strategies for two private label apparel brands of a large regional department store chain in the southeastern United States. The goal of the project was to revitalize product lines of the two brands by incorporating student ideas for new solutions, thereby giving the students practical experience with a real-life industry situation. There were a number of key players involved in the project. A privately-owned department store chain based in the southeastern United States which was seeking an academic partner had recognized a need to update two existing private label brands. They targeted middle-aged consumers looking for casual, moderately priced merchandise. The company was seeking to change direction with both packaging and presentation, and possibly product design. The branding and product development divisions of the company contacted professors in an academic department of a large southeastern state university. Two of the professors agreed that the task would be a good fit for their classes - one was a junior-level Intermediate Brand Management class; the other was a senior-level Fashion Product Development class. The professors felt that by working collaboratively on the project, students would be exposed to a real world scenario, within the security of an academic learning environment. Collaboration within an interdisciplinary team has the advantage of providing experiences and resources beyond the capabilities of a single student and adds "brainpower" to problem-solving processes (Lowman 2000). This goal of improving the capabilities of students directed the instructors in each class to form interdisciplinary teams between the Branding and Product Development classes. In addition, many universities are employing industry partnerships in research and teaching, where collaboration within temporal (semester) and physical (classroom/lab) constraints help to increase students' knowledge and experience of a real-world situation. At the University of Tennessee, the Center of Industrial Services and UT-Knoxville's College of Engineering worked with a company to develop design improvements in its U.S. operations. In this study, Because should be lower case b with a private label retail brand, Wickett, Gaskill and Damhorst's (1999) revised Retail Apparel Product Development Model was used by the product development and brand management teams. This framework was chosen because it addresses apparel product development from the concept to the retail stage. Two classes were involved in this project: a junior level Brand Management class and a senior level Fashion Product Development class. Seven teams were formed which included four students from Brand Management and two students from Product Development. The classes were taught the same semester, but not at the same time. At the beginning of the semester, each class was introduced to the industry partner and given the problem. Half the teams were assigned to the men's brand and half to the women's brand. The teams were responsible for devising approaches to the problem, formulating a timeline for their work, staying in touch with industry representatives and making sure that each member of the team contributed in a positive way. The objective for the teams was to plan, develop, and present a product line using merchandising processes (following the Wickett, Gaskill and Damhorst model) and develop new branding strategies for the proposed lines. The teams performed trend, color, fabrication and target market research; developed sketches for a line; edited the sketches and presented their line plans; wrote specifications; fitted prototypes on fit models, and developed final production samples for presentation to industry. The branding students developed a SWOT analysis, a Brand Measurement report, a mind-map for the brands and a fully integrated Marketing Report which was presented alongside the ideas for the new lines. In future if the opportunity arises to work in this collaborative way with an existing company who wishes to look both at branding and product development strategies, classes will be scheduled at the same time so that students have more time to meet and discuss timelines and assigned tasks. As it was, student groups had to meet outside of each class time and this proved to be a challenging though not uncommon part of teamwork (Pfaff and Huddleston, 2003). Although the logistics of this exercise were time-consuming to set up and administer, professors felt that the benefits to students were multiple. The most important benefit, according to student feedback from both classes, was the opportunity to work with industry professionals, follow their process, and see the results of their work evaluated by the people who made the decisions at the company level. Faculty members were grateful to have a "real-world" case to work with in the classroom to provide focus. Creative ideas and strategies were traded as plans were made, extending and strengthening the departmental links be tween the branding and product development areas. By working not only with students coming from a different knowledge base, but also having to keep in contact with the industry partner and follow the framework and timeline of industry practice, student teams were challenged to produce excellent and innovative work under new circumstances. Working on the product development and branding for "real-life" brands that are struggling gave students an opportunity to see how closely their coursework ties in with the real-world and how creativity, collaboration and flexibility are necessary components of both the design and business aspects of company operations. Industry personnel were impressed by (a) the level and depth of knowledge and execution in the student projects, and (b) the creativity of new ideas for the brands.