• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.151-171
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• 2014

Firms today have sought management effectiveness and efficiency utilizing information technologies (IT). Numerous firms are outsourcing specific information systems functions to cope with their short of information resources or IT experts, or to reduce their capital cost. Recently, Software-as-a-Service (SaaS) as a new type of information system has become one of the powerful outsourcing alternatives. SaaS is software deployed as a hosted and accessed over the internet. It is regarded as the idea of on-demand, pay-per-use, and utility computing and is now being applied to support the core competencies of clients in areas ranging from the individual productivity area to the vertical industry and e-commerce area. In this study, therefore, we seek to quantify the value that SaaS has on business performance by examining the relationships among firm strategies, SaaS technology maturity, and business performance of SaaS providers. We begin by drawing from prior literature on SaaS, technology maturity and firm strategy. SaaS technology maturity is classified into three different phases such as application service providing (ASP), Web-native application, and Web-service application. Firm strategies are manipulated by the low-cost strategy and differentiation strategy. Finally, we considered customer acquisition as a business performance. In this sense, specific objectives of this study are as follows. First, we examine the relationships between customer acquisition performance and both low-cost strategy and differentiation strategy of SaaS providers. Secondly, we investigate the mediating and moderating effects of SaaS technology maturity on those relationships. For this purpose, study collects data from the SaaS providers, and their line of applications registered in the database in CNK (Commerce net Korea) in Korea using a questionnaire method by the professional research institution. The unit of analysis in this study is the SBUs (strategic business unit) in the software provider. A total of 199 SBUs is used for analyzing and testing our hypotheses. With regards to the measurement of firm strategy, we take three measurement items for differentiation strategy such as the application uniqueness (referring an application aims to differentiate within just one or a small number of target industry), supply channel diversification (regarding whether SaaS vendor had diversified supply chain) as well as the number of specialized expertise and take two items for low cost strategy like subscription fee and initial set-up fee. We employ a hierarchical regression analysis technique for testing moderation effects of SaaS technology maturity and follow the Baron and Kenny's procedure for determining if firm strategies affect customer acquisition through technology maturity. Empirical results revealed that, firstly, when differentiation strategy is applied to attain business performance like customer acquisition, the effects of the strategy is moderated by the technology maturity level of SaaS providers. In other words, securing higher level of SaaS technology maturity is essential for higher business performance. For instance, given that firms implement application uniqueness or a distribution channel diversification as a differentiation strategy, they can acquire more customers when their level of SaaS technology maturity is higher rather than lower. Secondly, results indicate that pursuing differentiation strategy or low cost strategy effectively works for SaaS providers' obtaining customer, which means that continuously differentiating their service from others or making their service fee (subscription fee or initial set-up fee) lower are helpful for their business success in terms of acquiring their customers. Lastly, results show that the level of SaaS technology maturity mediates the relationships between low cost strategy and customer acquisition. That is, based on our research design, customers usually perceive the real value of the low subscription fee or initial set-up fee only through the SaaS service provide by vender and, in turn, this will affect their decision making whether subscribe or not.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.35-48
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• 2010

Today, the fashion market challenged by a maturing retail market needs a new paradigm in the "evolution of brand" to improve their comparative advantages. An important issue in fashion marketing is lifestyle brand extension with a specific aim to meet consumers' specific needs for their changing lifestyle. For fashion brand extensions into lifestyle product categories, Gen Y and Baby Boomer are emerging as "prospects"-Baby Boomers who are renovating their lifestyle, and generation Y experiencing changes in their life stage-with demands for buying new products. Therefore, it is imperative that apparel companies pay special attention to the consumer cohort for brand extension to create and manage their brand equity in a new product category. The purposes of this study are to (a) evaluate brand equity between parent and extension brands; (b) identify consumers' perceived marketing elements for brand extension; and (c) estimate a structural equation model for examining causative relationship between marketing elements and brand equity for brand extensions in lifestyle product category including home fashion items for the selected two groups (e.g., Gen Y, and Baby boomer). For theoretical frameworks, this study focused on the traditional marketing 4P's mix to identify what marketing element is more importantly related to brand extension equity for this study. It is assumed that comparable marketing capability can be critical to establish "brand extension equity", leads to successfully entering the new categories. Drawing from the relevant literature, this study developed research hypotheses incorporating brand equity factors and marketing elements by focusing on the selected consumers (e.g., Gen Y, Baby Boomer). In the context of brand extension in the lifestyle products, constructs of brand equity consist of brand awareness/association, brand perceptions (e.g., perceived quality, emotional value) and brand resonance adapted from CBBE factors (Keller, 2001). It is postulated that the marketing elements create brand extension equity in terms of brand awareness/association, brand perceptions by the brand extension into lifestyle products, which in turn influence brand resonance. For data collection, the sample was comprised of Korean female consumers in Gen Y and Baby Boomer consumer categories who have a high demand for lifestyle products due to changing their lifecycles. A total of 651 usable questionnaires were obtained from female consumers of Gen Y (n=326) and Baby Boomer (n=325) in South Korea. Structural and measurement models using a correlation matrix was estimated using LISREL 8.8. Findings indicated that perceived marketing elements for brand extension consisted of three factors: price/store image, product, and advertising. In the model of Gen Y consumers, price/store image had a positive effect on brand equity factors (e.g., brand awareness/association, perceived quality), while product had positive effect on emotional value in the brand extensions; and the brand awareness/association was likely to increase the perceived quality and emotional value, leading to brand resonance for brand extensions in the lifestyle products. In the model of Baby Boomer consumers, price/store image had a positive effect on perceived quality, which created brand resonance of brand extension; and product had a positive effect on perceived quality and emotional value, which leads to brand resonance for brand extension in the lifestyle products. However, advertising was negatively related to brand equity for both groups. This study provides an insight for fashion marketers in developing a successful brand extension strategy, leading to a sustainable competitive advantage. This study complements and extends prior works in the brand extension through critical factors of marketing efforts that affect brand extension success. Findings support a synergy effect on leveraging of fashion brand extensions (Aaker and Keller, 1990; Tauber, 1988; Shine et al., 2007; Pitta and Katsanis, 1995) in conjunction with marketing actions for entering into the new product category. Thus, it is recommended that marketers targeting both Gen Y and Baby Boomer can reduce marketing cost for entering the new product category (e.g., home furnishings) by standardized marketing efforts; fashion marketers can (a) offer extension lines with premium ranges of price; (b) place an emphasis on upscale features of store image positioning by a retail channel (e.g., specialty department store) in Korea, and (c) combine apparel with lifestyle product assortments including innovative style and designer’s limited editions. With respect to brand equity, a key to successful brand extension is consumers’ brand awareness or association that ensures brand identity with new product category. It is imperative for marketers to have knowledge of what contributes to more concrete associations in a market entry into new product categories. For fashion brands, a second key of brand extension can be a "luxury" lifestyle approach into new product categories, in that higher price or store image had impact on perceived quality that established brand resonance. More importantly, this study increases the theoretical understanding of brand extension and suggests directions for marketers as they establish marketing program at Gen Y and Baby Boomers.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

• Journal of Global Scholars of Marketing Science
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• v.19 no.3
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• pp.5-16
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• 2009

Corporate social responsibility (CSR) activities have been shown to be potential factors that can improve corporate image and increase the ability of corporations to compete. However, most previous studies related to CSR activities investigated how these activities influence product and corporate evaluation, as well as corporate image. In addition, some researchers treated consumers' perceptions of corporate motives as moderator variables in evaluating the relationship between corporate social responsibilities and consumer response. However, motive-based theories have some weaknesses. Corporate social responsibility activities cause two motives(egoistic vs. altruistic) for consumers, but recently, Vlachos et al. (2008) argued that these motives should be segmented. Thus, it is possible to transform the original theory into a modified theory model (persuasion knowledge model, PKM). Vlachos et al. (2008) segmented corporate social responsibility motives into four types and compared the effects of these motives on customer loyalty. Prior studies have proved that CSR activities with positive motives have positive influences on customer loyalty. However, the psychological reasons underlying this finding have not been determined empirically. Thus, the objectives of this research are twofold. First, we attempt to determine why most customers favor companies that they feel have positive motives for their corporate social responsibility activities. Second, we attempt to measure the effects of consumers' reciprocity when society benefits from corporate social responsibility activities. The following research hypotheses are constructed. H1: Values-driven motives for corporate social responsibility activities have a positive influence on the perceived reciprocity. H2: Stakeholder-driven motives for corporate social responsibility activities have a negative influence on the perceived reciprocity. H3: Egoistic-driven motives for corporate social responsibility activities have a negative influence on perceived reciprocity. H4: Strategic-driven motives for corporate social responsibility activities have a negative influence on perceived reciprocity. H5: Perceived reciprocity for corporate social responsibility activities has a positive influence on consumer loyalty. A single company is selected as a research subject to understand how the motives behind corporate social responsibility influence consumers' perceived reciprocity and customer loyalty. A total sample of 200 respondents was selected for a pilot test. In addition, to ensure a consistent response, we ensured that the respondents were older than 20 years of age. The surveys of 172 respondents (males-82, females-90) were analyzed after 28 invalid questionnaires were excluded. Based on our cutoff criteria, the model fit the data reasonably well. Values-driven motives for corporate social responsibility activities had a positive effect on perceived reciprocity (t = 6.75, p < .001), supporting H1. Morales (2005) also found that consumers appreciate a company's social responsibility efforts and the benefits provided by these efforts to society. Stakeholder-driven motives for corporate social responsibility activities did not affect perceived reciprocity (t = -.049, p > .05). Thus, H2 was rejected. Egoistic-driven motives (t = .3.11, p < .05) and strategic-driven (t = -4.65, p < .05) motives had a negative influence on perceived reciprocity, supporting H3 and H4, respectively. Furthermore, perceived reciprocity had a positive influence on consumer loyalty (t = 4.24, p < .05), supporting H5. Thus, compared with the general public, undergraduate students appear to be more influenced by egoistic-driven motives. We draw the following conclusions from our research findings. First, value-driven attributions have a positive influence on perceived reciprocity. However, stakeholder-driven attributions have no significant effects on perceived reciprocity. Moreover, both egoistic-driven attributions and strategic-driven attributions have a negative influence on perceived reciprocity. Second, when corporate social responsibility activities align with consumers' reciprocity, the efforts directed towards social responsibility activities have a positive influence on customer loyalty. In this study, we examine whether the type of motivation affects consumer responses to CSR, and in particular, we evaluate how CSR motives can influence a key internal factor (perceived reciprocity) and behavioral consumer outcome (customer loyalty). We demonstrate that perceived reciprocity plays a mediating role in the relationship between CSR motivation and customer loyalty. Our study extends the research on consumer CSR-inferred motivations, positing them as a direct indicator of consumer responses. Furthermore, we convincingly identify perceived reciprocity as a sub-process mediating the effect of CSR attributions on customer loyalty. Future research investigating the ultimate behavior and financial impact of CSR should consider that the impacts of CSR also stem from perceived reciprocity. The results of this study also have important managerial implications. First, the central role that reciprocity plays indicates that managers should routinely measure how much their socially responsible actions create perceived reciprocity. Second, understanding how consumers' perceptions of CSR corporate motives relate to perceived reciprocity and customer loyalty can help managers to monitor and enhance these consumer outcomes through marketing initiatives and management of CSR-induced attribution processes. The results of this study will help corporations to understand the relative importance of the four different motivations types in influencing perceived reciprocity.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

• Journal of agricultural medicine and community health
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• v.22 no.1
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• pp.1-18
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• 1997

This study was carried out to reveal the status of health examination among farmers and to attract more attention to the health care system for farmers. Ten pre-trained medical students interviewed the rural residents 18 years of age and older in eight villages which were randomly selected from a county near Taegu city in Korea, in August 1996. Finally 751 persons were interviewed of whom the percentages of male and female were 41.8%, 58.2% respectively. Among the subjects, 361(48.3%) were fully engaged in farming, 184(24.4%) were partly engaged, and the remaining 206(27.3%) were not engaged in farming at all. The overall prevalence of farmer's disease was 23.0% and there was no significant difference between the group of fully engaged in farming(23.3%) and the group of not-fully engaged(22.9%). But the prevalence of farmer's disease in female subjects(27.8%) was significantly higher than that in male(16.2%)(p<0.01). Among the 288 farmer engaged in spraying pesticide, 113(39.2%) had experienced one or more pesticide related symptoms during last one year, but only 18(15.9%) of them had visited medical facilities due to their symptoms. The experience of receiving education about pesticide was significantly correlated with the degree of wearing protectors during pesticide spraying(p<0.001). Among the 736 persons excluding non-respondents, 281(38.2%) received health examination during last one year ; 176(62.6%) of them received free health examination, and 105(37.4%) received charged one. Among the 533 persons 40 years age and older, only 124(23.3%) had received the 'health examination for the elderly' during last one year, which is provided for the 40 years age and older by Korea medical insurance corporation and medical insurance societies. Most of all beneficiaries of self-employed medical insurance thought the imposed contributions as very expensive(77.4%) or moderately expensive(13.2%). The great majority of farmers are exposed to various health risk factors including pesticide, high temperature, overwork etc. comparable to industrial workers. But farmers are excluded from the regular yearly worker's health examination because of not belonging to a company despite they pay relatively more medical insurance contributions compared with the industrial workers and the urban self-employed medical insureds. It is necessary to develop special health management program for farmers such as the special health examination for the industrial workers exposed harmful agents.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.137-152
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• 2012

In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

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

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

• Journal of Genetic Medicine
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• v.8 no.2
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• pp.89-99
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• 2011

Over the years Korean health care system has improved in delivery of quality care to the general population for many areas of the health problems. The system is now being recognized in the world as the most cost effective one. It is covered by the uniform national health insurance policy for which most people in Korea are mandatory policy holders. Genetic counseling service, however, which is well recognized as an integral part of clinical genetics service deals with diagnosis and management of genetic condition as well as genetic information presentation and family support, is yet to be delivered in comprehensive way for the patients and families in need. Two major obstacles in providing genetic counseling service in korean health care system are identified; One is the lack of recognition for the need for genetic counseling service as necessary service by the national health insurance. Genetic counseling consumes a significant time in delivery and the current very low-fee schedule for physician service makes it very difficult to provide meaningful service. Second is the critical shortage of qualified professionals in the field of medical genetics and genetic counseling who can provide the service of genetic counseling in clinical setting. However, recognition and understanding of the fact that the scope and role of genetic counseling is expanding in post genomic era of personalized medicine for delivery of quality health care, will lead to the efforts to overcome obstacles in providing genetic counseling service in korean health care system. Only concerted efforts from health care policy makers of government on clinical genetics service and genetic counseling for establishing adequate reimbursement coverage and professional communities for developing educational program and certification process for professional genetic counselors, are necessary for the delivery of much needed clinical genetic counseling service in Korea.