• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.345-352
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• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4400-4419
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• 2021

In the 4th Industrial Revolution, the healthcare industry is undergoing a paradigm shift from post-care and management systems based on diagnosis and treatment to disease prevention and management based on personal precision medicine. To optimize medical services for individual patients, an open ecosystem for the healthcare industry that allows the exchange and utilization of personal health records (PHRs) is required. However, under the current system of hospital-centered data management, it is difficult to implement the linking and sharing of PHRs in practice. To address this problem, in this study, we present the design and implementation of a patient-centered PHR platform using blockchain technology. This platform achieved transparency and reliability in information management by eliminating the risk of leakage and tampering/altering personal information, which could occur when using a PHR. In addition, the patient-consent system was applied to a PHR; thus, the patient acted as the user with ownership. The proposed blockchain-based PHR platform enables the integration of personal medical information with scattered distribution across multiple hospitals, and allows patients to freely use their health records in their daily lives and emergencies. The proposed platform is expected to serve as a stepping stone for patient-centered healthcare data management and utilization.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.167-174
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• 2023

This paper attempted to improve the accuracy of the colorectal cancer diagnosis model using image data augmentation in convolutional neural network. Image data augmentation was performed by flipping, rotation, translation, shearing and zooming with basic image manipulation method. This study split 4000 training data and 1000 test data for 5000 image data held, the model is learned by adding 4000 and 8000 images by image data augmentation technique to 4000 training data. The evaluation results showed that the clasification accuracy for 4000, 8000, and 12,000 training data were 85.1%, 87.0%, and 90.2%, respectively, and the improvement effect depending on the increase of image data was confirmed.

• Journal of Venture Innovation
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• v.3 no.1
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• pp.47-66
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• 2020

Even a lot of research has shown about entrepreneurship, there are still some limitations to apply to women. It was limited because it has been illuminated by general points of view. In this situation, it is needed to study more about entrepreneurship education and entrepreneurship will. The purpose of this study is to examine the impact of entrepreneurship education experience and the effect of entrepreneurship education on the will of entrepreneurship. First, the implications of the study were to verify that the effect of entrepreneurship education, which is the ability of entrepreneurship knowledge, can contribute to raising the will of entrepreneurship. Therefore, in the entrepreneurship education conducted by universities, operating as a participatory program that can enhance entrepreneurship skills rather than theoretical aspects will contribute to enhancing the willingness of female college students to start up. Second, career orientation and entrepreneurship, which are characteristics of individual psychological behavior, have the result of raising the will to start a business and linking it to actual start-up. Therefore, it is suggested that students conduct their entrepreneurship comprehension and connect themselves to active entrepreneurship activities by conducting a diagnosis of the career orientation and entrepreneurship of students during the start-up education of the university. Third, women's entrepreneurship will become more active in government, and university entrepreneurship support programs expanded to target women's college students.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• Journal of Distribution Science
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• v.14 no.3
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• pp.63-71
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• 2016

Purpose - A variety of indicators are used for the diagnosis of economic situation. However, most indicators explain the past economic situation because of the time difference between the measurement and announcement. This study aims to argue for the resurrection of an idea: electricity demand can be used as an indicator of economic activity. In addition, this study made an endeavor to develop a new Real Business Index(RBI) which could quickly represent the real economic condition based on the sales statistics of industrial and public electricity. Research design, data, and methodology - In this study monthly sales of industrial and public electricity from 2000 to 2015 was investigated to analyze the relationship between the economic condition and the amount of electricity consumption and to develop a new Real Business Index. To formulate the Index, this study followed next three steps. First, we decided the explanatory variables, period, and collected data. Second, after calculating the monthly changes for each variable, standardization and estimating the weighted value were conducted. Third, the computation of RBI finalized the development of empirical model. The principal component analysis was used to evaluate the weighted contribution ratio among 3 sectors and 17 data. Hodrick-Prescott filter analysis was used to verify the robustness of out model. Results - The empirical results are as follows. First, compatibility of the predictability between the new RBI and the existing monthly cycle of coincident composite index was extremely high. Second, two indexes had a high correlation of 0.7156. In addition, Hodrick-Prescott filter analysis demonstrated that two indexed also had accompany relationship. Third, when the changes of two indexes were compared, they were found that the times when the highest and the lowest point happened were similar, which suggested that it is possible to use the new RBI index as a complementing indicator in a sense that the RBI can explain the economic condition almost in real time. Conclusion - A new economic index which can explain the economic condition needs to be developed well and rapidly in a sense that it is useful to determine accurately the current economic condition to establish economic policy and corporate strategy. The salse of electricity has a close relationship with economic conditions because electricity is utilized as a main resource of industrial production. Furthermore, the result of the sales of electricity can be gathered almost in real time. This study applied the econometrics model to the statistics of the sales of industrial and public electricity. In conclusion, the new RBI index was highly related with the existing monthly economic indexes. In addition, the comparison between the RBI index and other indexes demonstrated that the direction of the economic change and the times when the highest and lowest points had happened were almost the same. Therefore, this RBI index can become the supplementary indicator of the official indicators published by Korean Bank or the statistics Korea.

• The Korean Journal of Franchise Management
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• v.5 no.1
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• pp.109-141
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• 2014

Recently, the interests of consumers in firms that implement the social commitment activities have been consistently growing. Consumers' evaluation about the level of corporate social responsibility(CSR) can affect the overall image for product or service of corporation. This recent changes make a marketer to have to consider direct and indirect effects of CSR efforts on the market performance. This phenomena is also found in the franchise industry. The importance of CSR is more critical rather than other industries since each franchisor should care franchisees as well as end users. Franchisors' execution of CSR could increase satisfaction of end user through consonance of activities provided by franchisees. However most franchisor stay in focusing on the traditional CSR activities. Therefore, this study aims to enhance the understanding the CSR in franchise and provide the phase model of CSR development for general firms including franchise. After diagnosis the firms with the proposed model, the study found many franchisors have huge gap between current CSR activities and higher level of CSR policies that franchisor have to make facing. This study call franchisors to reduce this gap by implementing new CSR efforts. If they answer for this calling, franchise industry could leap for making the best practice of creating shared value with other stakeholders.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.

• Journal of Distribution Research
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• v.15 no.3
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• pp.1-34
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• 2010

Introduction The purpose of this research is to develop overall model which involves the effect of ongoing support services by franchisor on franchisee's relationship quality(trust, satisfaction, and commitment) and business performance(financial and non-financial performance), and to investigate the relationships among trust, satisfaction, commitment, financial and non-financial performance. This study also suggests franchise business or franchise system should be based on long-term orientation between franchisor and franchisee rather than short-term orientation, or transactional relationship, and proposes the most effective way of providing on-going support services by franchisor with franchisee thru symbiotic relationship among franchisor and franchisee Research Model and Hypothesis The research model as Figure 1 shows the variables on-going support services which affect the relationship quality between franchisor and franchisee such as trust, satisfaction, and commitment, and also analyze the effects of relationship quality on business performance including financial and non-financial performance We established 12 hypotheses to test as follows; Relationship between on-going support services and trust H1: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's trust. Relationship between on-going support services and satisfaction H2: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's satisfaction. Relationship between on-going support services and commitment H3: On-going support services factors (product category & price, logistics service, promotion, information providing & problem solving capability, supervisor's support, and education & training support) have positive effect on franchisee's commitment. Relationship among relationship quality: trust, satisfaction, and commitment H4: Franchisee's trust has positive effect on franchisee's satisfaction. H5: Franchisee's trust has positive effect on franchisee's commitment. H6: Franchisee's satisfaction has positive effect on franchisee's commitment. Relationship between relationship quality and business performance H7: Franchisee's trust has positive effect on franchisee's financial performance. H8: Franchisee's trust has positive effect on franchisee's non-financial performance. H9: Franchisee's satisfaction has positive effect on franchisee's financial performance. H10: Franchisee's satisfaction has positive effect on franchisee's non-financial performance. H11: Franchisee's commitment has positive effect on franchisee's financial performance. H12: Franchisee's commitment has positive effect on franchisee's non-financial performance. Method The on-going support services were defined as an organized system of continuous supporting services by franchisor for the purpose of satisfying the expectation of franchisee based on long-term orientation and classified into six constructs such as product category & price, logistics service, promotion, providing information & problem solving capability, supervisor's support, and education & training support. The six constructs were measured agreement using a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree)as follows. The product category & price was measured by four items: menu variety, price of food material provided by franchisor, and support for developing new menu. The logistics service was measured by six items: distribution system of franchisor, return policy for provided food materials, timeliness, inventory control level of franchisor, accuracy of order, and flexibility of emergency order. The promotion was measured by five items: differentiated promotion activities, brand image of franchisor, promotion effect such as customer increase, long-term plan of promotion, and micro-marketing concept in promotion. The providing information & problem solving capability was measured by information providing of new products, information of competitors, information of cost reduction, and efforts for solving problems in franchisee's operations. The supervisor's support was measured by supervisor operations, frequency of visiting franchisee, support by data analysis, processing the suggestions by franchisee, diagnosis and solutions for the franchisee's operations, and support for increasing sales in franchisee. Finally, the of education & training support was measured by recipe training by specialist, service training for store people, systemized training program, and tax & human resources support services. Analysis and results The data were analyzed using Amos. Figure 2 and Table 1 present the result of the structural equation model. Implications The results of this research are as follows: Firstly, the factors of product category, information providing and problem solving capacity influence only franchisee's satisfaction and commitment. Secondly, logistic services and supervising factors influence only trust and satisfaction. Thirdly, continuing education and training factors influence only franchisee's trust and commitment. Fourthly, sales promotion factor influences all the relationship quality representing trust, satisfaction, and commitment. Fifthly, regarding relationship among relationship quality, trust positively influences satisfaction, however, does not directly influence commitment, but satisfaction positively affects commitment. Therefore, satisfaction plays a mediating role between trust and commitment. Sixthly, trust positively influence only financial performance, and satisfaction and commitment influence positively both financial and non-financial performance.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.