• Journal of Service Research and Studies
• /
• v.13 no.2
• /
• pp.99-113
• /
• 2023

In a customer-based industry, customer retention is the competitiveness of a company, and improving customer retention improves the competitiveness of the company. Therefore, accurate prediction and management of potential dormant customers is paramount to increasing the competitiveness of the enterprise. In particular, there are numerous competitors in the domestic card industry, and the government is introducing an automatic closing system for dormant card management. As a result of these social changes, the card industry must focus on better predicting and managing potential dormant cards, and better predicting dormant customers is emerging as an important challenge. In this study, the Recurrent Neural Network (RNN) methodology was used to predict potential dormant customers in the card industry, and in particular, Long-Short Term Memory (LSTM) was used to efficiently learn data for a long time. In addition, to redefine the variables needed to predict dormant customers in the card industry, Unified Theory of Technology (UTAUT), an integrated technology acceptance theory, was applied to redefine and group the variables used in the model. As a result, stable model accuracy and F-1 score were obtained, and Hit-Ratio proved that models using LSTM can produce stable results compared to other algorithms. It was also found that there was no moderating effect of demographic information that could occur in UTAUT, which was pointed out in previous studies. Therefore, among variable selection models using UTAUT, dormant customer prediction models using LSTM are proven to have non-biased stable results. This study revealed that there may be academic contributions to the prediction of dormant customers using LSTM algorithms that can learn well from previously untried time series data. In addition, it is a good example to show that it is possible to respond to customers who are preemptively dormant in terms of customer management because it is predicted at a time difference with the actual dormant capture, and it is expected to contribute greatly to the industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.276-279
• /
• 2022

The 4th industrial revolution refers to the transition to a knowledge society in which the production speed of knowledge is fast and the proportion of the knowledge industry is greatly increased. The reorganization of the industrial structure and the change of occupations and jobs due to new technologies are bringing about changes in education, and the development of digital technology has made education that is borderless, individual and dynamic, becoming the new standard of education. With these changes, interest in nano-degrees on new technologies or micro-degrees focused on core courses rather than regular course degrees is increasing. As a representative example, Udacity in the United States has opened and operated online nanodegree courses related to jobs, and collaborates with major companies to develop and educate core training courses necessary for companies, thereby efficiently supporting companies in securing talent. With the revitalization of online vocational and job training, an environment has been prepared in which individuals can set goals for vocational competency development and continue their portfolio-based sustainable learning. However, for effective vocational education, automated and personalized educational content design should be preceded. To this end, in this paper, we propose a personalized career and course map recommendation system in the era of online learning

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.73-81
• /
• 2016

In general, infrared images have less sharpness and image details than visible images. So, the prior image upscaling methods are not effective in the infrared images. In order to solve this problem, this paper proposes an algorithm which initially up-scales an input infrared (IR) image by using adaptive dynamic range encoding (ADRC)-based super-resolution (SR) method, and then fuses the result with the corresponding visible images. The proposed algorithm consists of a up-scaling phase and a fusion phase. First, an input IR image is up-scaled by the proposed ADRC-based SR algorithm. In the dictionary learning stage of this up-scaling phase, so-called 'pre-emphasis' processing is applied to training-purpose high-resolution images, hence better sharpness is achieved. In the following fusion phase, high-frequency information is extracted from the visible image corresponding to the IR image, and it is adaptively weighted according to the complexity of the IR image. Finally, a up-scaled IR image is obtained by adding the processed high-frequency information to the up-scaled IR image. The experimental results show than the proposed algorithm provides better results than the state-of-the-art SR, i.e., anchored neighborhood regression (A+) algorithm. For example, in terms of just noticeable blur (JNB), the proposed algorithm shows higher value by 0.2184 than the A+. Also, the proposed algorithm outperforms the previous works even in terms of subjective visual quality.

• Journal of the Korean Society of Radiology
• /
• v.17 no.1
• /
• pp.131-139
• /
• 2023

In this study, we are purposed to find the educational effect of practical training using a 3D printed structure-based blind box on multidimensional radiographic image interpretation. The subjects were 83 (male: 49, female: 34) 2nd year radiological science students who participated in the digital medical imaging practice that was conducted for 3 years from 2020 to 2022. The learning method used 3D printing technology to print out the inside structure of the blind box designed by itself. After taking X-rays 3 times (x, y, z axis), the structure images in the blind box were analyzed for each small group. We made the 3D structure that was self-made with clay based on our 2D radiographic images. After taking X-rays of the 3D structure, it was compared whether it matches the structural image of the blind box. The educational effect for the practical training surveyed class faithfulness, radiographic image interpretation ability (attenuation concept, contrast concept, windowing concept, 3-dimensional reading ability), class satisfaction (interest, external recommendation, immersion) on a 5-point Likert scale as an anonymous student self-writing method. As a result, all evaluation items had high positive effects without significant differences between males and females. Practical education using blind boxes is a meaningful example of radiology education technology using 3D printing technology, and it is expected to be used as content to improve students' problem-solving skills and increase satisfaction with major subjects.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.217-242
• /
• 2022

With the launch of Artificial Intelligence(AI)-based intelligent products on the market, innovative changes are taking place not only in business but also in consumers' daily lives. Intelligent products have the potential to realize technology differentiation and increase market competitiveness through advanced functions of artificial intelligence. However, there is no new product development methodology that can sufficiently reflect the characteristics of artificial intelligence for the purpose of developing intelligent products with high market acceptance. This study proposes a KANO-QFD integrated model as a methodology for intelligent product development. As a specific example of the empirical analysis, the types of consumer requirements for hair loss prediction and treatment device were classified, and the relative importance and priority of engineering characteristics were derived to suggest the direction of intelligent medical product development. As a result of a survey of 130 consumers, accurate prediction of future hair loss progress, future hair loss and improved future after treatment realized and viewed on a smartphone, sophisticated design, and treatment using laser and LED combined light energy were realized as attractive quality factors among the KANO categories. As a result of the analysis based on House of Quality of QFD, learning data for hair loss diagnosis and prediction, micro camera resolution for scalp scan, hair loss type classification model, customized personal account management, and hair loss progress diagnosis model were derived. This study is significant in that it presented directions for the development of artificial intelligence-based intelligent medical product that were not previously preceded.

• Journal of Global Scholars of Marketing Science
• /
• v.18 no.4
• /
• pp.61-118
• /
• 2008

The purpose of this research is to construct research framework for international franchising based on existing literature and to identify research components in the framework. Franchise can be defined as management styles that allow franchisee use various management assets of franchisor in order to make or sell product or service. It can be divided into product distribution franchise that is designed to sell products and business format franchise that is designed for running it as business whatever its form is. International franchising can be defined as a way of internationalization of franchisor to foreign country by providing its business format or package to franchisee of host country. International franchising is growing fast for last four decades but academic research on this is quite limited. Especially in Korea, research about international franchising is carried out on by case study format with single case or empirical study format with survey based on domestic franchise theory. Therefore, this paper tries to review existing literature on international franchising research, providing research framework, and then stimulating new research on this field. International franchising research components include motives and environmental factors for decision of expanding to international franchising, entrance modes and development plan for international franchising, contracts and management strategy of international franchising, and various performance measures from different perspectives. First, motives of international franchising are fee collection from franchisee. Also it provides easier way to expanding to foreign country. The other motives including increase total sales volume, occupying better strategic position, getting quality resources, and improving efficiency. Environmental factors that facilitating international franchising encompasses economic condition, trend, and legal or political factors in host and/or home countries. In addition, control power and risk management capability of franchisor plays critical role in successful franchising contract. Final decision to enter foreign country via franchising is determined by numerous factors like history, size, growth, competitiveness, management system, bonding capability, industry characteristics of franchisor. After deciding to enter into foreign country, franchisor needs to set entrance modes of international franchising. Within contractual mode, there are master franchising and area developing franchising, licensing, direct franchising, and joint venture. Theories about entrance mode selection contain concepts of efficiency, knowledge-based approach, competence-based approach, agent theory, and governance cost. The next step after entrance decision is operation strategy. Operation strategy starts with selecting a target city and a target country for franchising. In order to finding, screening targets, franchisor needs to collect information about candidates. Critical information includes brand patent, commercial laws, regulations, market conditions, country risk, and industry analysis. After selecting a target city in target country, franchisor needs to select franchisee, in other word, partner. The first important criteria for selecting partners are financial credibility and capability, possession of real estate. And cultural similarity and knowledge about franchisor and/or home country are also recognized as critical criteria. The most important element in operating strategy is legal document between franchisor and franchisee with home and host countries. Terms and conditions in legal documents give objective information about characteristics of franchising agreement for academic research. Legal documents have definitions of terminology, territory and exclusivity, agreement of term, initial fee, continuing fees, clearing currency, and rights about sub-franchising. Also, legal documents could have terms about softer elements like training program and operation manual. And harder elements like law competent court and terms of expiration. Next element in operating strategy is about product and service. Especially for business format franchising, product/service deliverable, benefit communicators, system identifiers (architectural features), and format facilitators are listed for product/service strategic elements. Another important decision on product/service is standardization vs. customization. The rationale behind standardization is cost reduction, efficiency, consistency, image congruence, brand awareness, and competitiveness on price. Also standardization enables large scale R&D and innovative change in management style. Another element in operating strategy is control management. The simple way to control franchise contract is relying on legal terms, contractual control system. There are other control systems, administrative control system and ethical control system. Contractual control system is a coercive source of power, but franchisor usually doesn't want to use legal power since it doesn't help to build up positive relationship. Instead, self-regulation is widely used. Administrative control system uses control mechanism from ordinary work relationship. Its main component is supporting activities to franchisee and communication method. For example, franchisor provides advertising, training, manual, and delivery, then franchisee follows franchisor's direction. Another component is building franchisor's brand power. The last research element is performance factor of international franchising. Performance elements can be divided into franchisor's performance and franchisee's performance. The conceptual performance measures of franchisor are simple but not easy to obtain objectively. They are profit, sale, cost, experience, and brand power. The performance measures of franchisee are mostly about benefits of host country. They contain small business development, promotion of employment, introduction of new business model, and level up technology status. There are indirect benefits, like increase of tax, refinement of corporate citizenship, regional economic clustering, and improvement of international balance. In addition to those, host country gets socio-cultural change other than economic effects. It includes demographic change, social trend, customer value change, social communication, and social globalization. Sometimes it is called as westernization or McDonaldization of society. In addition, the paper reviews on theories that have been frequently applied to international franchising research, such as agent theory, resource-based view, transaction cost theory, organizational learning theory, and international expansion theories. Resource based theory is used in strategic decision based on resources, like decision about entrance and cooperation depending on resources of franchisee and franchisor. Transaction cost theory can be applied in determination of mutual trust or satisfaction of franchising players. Agent theory tries to explain strategic decision for reducing problem caused by utilizing agent, for example research on control system in franchising agreements. Organizational Learning theory is relatively new in franchising research. It assumes organization tries to maximize performance and learning of organization. In addition, Internalization theory advocates strategic decision of direct investment for removing inefficiency of market transaction and is applied in research on terms of contract. And oligopolistic competition theory is used to explain various entry modes for international expansion. Competency theory support strategic decision of utilizing key competitive advantage. Furthermore, research methodologies including qualitative and quantitative methodologies are suggested for more rigorous international franchising research. Quantitative research needs more real data other than survey data which is usually respondent's judgment. In order to verify theory more rigorously, research based on real data is essential. However, real quantitative data is quite hard to get. The qualitative research other than single case study is also highly recommended. Since international franchising has limited number of applications, scientific research based on grounded theory and ethnography study can be used. Scientific case study is differentiated with single case study on its data collection method and analysis method. The key concept is triangulation in measurement, logical coding and comparison. Finally, it provides overall research direction for international franchising after summarizing research trend in Korea. International franchising research in Korea has two different types, one is for studying Korean franchisor going overseas and the other is for Korean franchisee of foreign franchisor. Among research on Korean franchisor, two common patterns are observed. First of all, they usually deal with success story of one franchisor. The other common pattern is that they focus on same industry and country. Therefore, international franchise research needs to extend their focus to broader subjects with scientific research methodology as well as development of new theory.

• Journal of the Korean Chemical Society
• /
• v.68 no.5
• /
• pp.259-273
• /
• 2024

This study aimed to investigate changes in metamodeling recognition among chemistry teachers through a teacher educational program related to redox models and water electrolysis experiments. To this end, a science model education program was developed for 9 chemistry teachers and conducted over 10 lessons for a total of 40 hours. In addition, a pre- and post-survey was administered to determine teachers' metamodeling recognition in non-contextual and contextual situations. As a result of the study, through the science model education program, teachers showed educational effects in both non-contextual and contextual situations. In the case of non-contextual situations, the stages of scientific metamodeling knowledge development of chemistry teachers came out differently depending on the type of question. For example, the nature or purpose of the model, the modeling process, or the evaluation and improvement of the model improved from low to high, but there was no significant change because the perception of model change and diversity was already high in advance. In the case of contextual situations, the stage of scientific metamodeling knowledge development improved from objectivity to subjectivity in both the redox model theory class and the water electrolysis model experiment class. Therefore, through the 5E circular learning model-based education program, chemistry teachers' perception of metamodeling was clearly improved. However, the modeling activities of teachers in the water electrolysis model experiment class were different from the change in metamodeling perception. The types that teachers selected as additional experiments for modeling were analyzed in two ways. The first type is when they are interested in finding an ideal condition in which the ratio of hydrogen and oxygen gas is close to 2:1 through additional experiments. The second type is when additional experiments are designed with interest in why the experimental results are coming out like that. It was analyzed that the second type was the experiment necessary for modeling. In addition, modeling activities were analyzed into two types. The first was a type in which water molecules were directly decomposed in two electrodes to generate hydrogen and oxygen gas. This type was the case of regression to the textbook model regardless of the experimental results, and 6 chemistry teachers out of 8 were analyzed as this type. The second type was the type in which water reacted at the (+) electrode to generate other substances, and hydrogen ions reacted at the (-) electrode to generate hydrogen gas. Teachers who performed these modeling activities corresponded to the second type in additional experiments, and 2 chemistry teachers out of 8 corresponded to this. Therefore, it is necessary to provide an experience of activities corresponding to the second type of experiment and modeling through an educational program that provides an experience of directly modeling through experiments in order to develop modeling capabilities, unlike the development of metamodeling knowledge.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.95-112
• /
• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.133-148
• /
• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.