• Journal of Digital Convergence
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• 제15권4호
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• pp.327-337
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• 2017

The purpose of this study is to identify the current state of paid care services and to identify the factors that affect the utilization of private nursing services. This study constructed and utilized the Korean Health Panel data (2011-2014) in the form of panel data, and selected 5,110 patients who had experience using one or more hospitalization services per year. STATA 12.0 SE was used for data processing and analysis of this study. Frequency analysis was performed to confirm basic characteristics of hospitalized patients. Cross-analysis and t-test were conducted to confirm the status of paid care services according to characteristics. Respectively. Finally, panel logistic regression was performed by applying a hierarchical method to stepwise modeling the three categories of Andersen's Behavioral Model to identify factors affecting the use of paid care services for inpatients. The results showed that the use of paid nursing services was higher in women, elderly, long - term hospitalized and disabled. On the other hand, significant household income variables in private employment did not show significant results. The results of this study are expected to be used as basic data for the selection of the nursing care integrated services under discussion. In addition, detailed discussions on the selection of subjects should be made in the future.

• Journal of Digital Convergence
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• 제11권3호
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• pp.165-173
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• 2013

The purpose of this study was to investigate the effects of customer's innovation and usefulness on the use intention of Service Robots by potential customers. Based on Davis(1989)'s Technology Acceptance Model, this study formulated three hypotheses, which were about relationships between customer's innovation, usefulness, and use intention of Service Robots. For this study, structured questionnaires were used and data were collected from the 171 people in Seoul and Cheonan. To test three hypotheses, collected data were analyzed using hierarchical regression technique. The results showed that customer's innovation has no influence on usefulness, whereas customer's innovation and usefulness have effects on use intention.

• Journal of Intelligence and Information Systems
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• 제19권2호
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Industrial Convergence
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• 제18권2호
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• pp.37-44
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• 2020

This paper of descriptive-correlative design determined the effects of health perception, need of exercise and self-efficacy on the frequency of exercise among diabetic patients. A total of 86 questionnaires were retrieved from these groups of diabetic patients, who regularly visits endocrinology clinics and accomplished voluntarily the major instrument distributed from two university hospitals in A city, during June to July, 2018. The result was a significant positive relationship that existed between health perception (r=.215, p=.043) and self-efficacy (r=.440, p<.001) with frequency of exercise respectively. Regression analysis revealed that the factor affecting the frequency of exercise is self efficacy (β=.440, p<.001) which illustrated to have a significant effect in the model's explanatory power at 18.4%(F=20.836, p<.001) results. This suggests that diabetic patients with a high positive health perception increase self-efficacy, their self-efficacy will help increase the frequency of exercise if further developed. Therefore, if an intervention program is developed to improve the health perception and self-efficacy education program for diabetics, it will help improve the frequency of exercise, namely diabetes management exercise.

• Journal of the Korean Institute of Intelligent Systems
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• 제26권3호
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• pp.218-225
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• 2016

It is important to select the personnel with ideal pilot aptitude considering dramatically advancing aircraft performance and complexity of military operations as a consequence to the highly developed science and technology. The opportunity cost lost from dropouts and human error being the first cause of aviation accidents are the realistic reasons for the significance of personnel selection based on their aptitude. This study analyses the ROKAF pilot aptitude test that was improved in 2004, using various classification models. This study discusses the significance of the selected variables along with the direction of ROKAF pilot aptitude test for its development in the future. The accuracy of the classification models was improved by taking into account differing personnel characteristics of individuals on the test.

• Journal of the Korean School Mathematics Society
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• 제10권1호
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• pp.113-127
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• 2007

This study explored the possibility of building a statistical model predicting difficulty of mathematics test items through the analysis of nation-wide scholastic ability test results for the past 5 years. Multiple linear regression analysis was conducted in predicting difficulty of mathematics test items. We adopted three major areas for independent variables: the content area, the behavior area, and the test item format area, each of which was categorized into more detailed sub-areas. For the dependent variable, the proportion of correct answer was used to represent the item difficulty. Statistically significant independent variables were included in the regression model based on the stepwise selection method. Several important factors affecting difficulty of mathematics test items for each area were identified. R-squares for the final regression model were fairly high, implying that the regression equation can be used to predict difficulty of test items at an acceptable level. Lastly, the regression model was cross-validated using independently collected data. We believe that this study will provide basic but very critical information for predicting the proportion of correct answer by showing the factors that should be considered for developing mathematics test items for the college entrance examination or high school classroom test.

• Journal of Korea Technology Innovation Society
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• 제19권2호
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• pp.254-279
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• 2016

Recently, with the conversion towards knowledge-based economy era, the importance of the evaluation for patent valuation has been growing rapidly because technology transactions are increasing with the purpose of practically utilizing R&D outcomes such as technology commercialization and technology transfer. Nevertheless, there is a lack of research on determinants of patent valuation by analyzing technology transactions due to the difficulty of collecting data in practice. Hence, to suggest quantitative determinants for the patent valuation which could be applied to scoring methods, 15 patent valuation models domestically and overseas are analysed in order to assure the objectiveness for subjective results from qualitative methods such as expert surveys, comparison assessment, etc. Through this analysis, the important 6 common determinants are drawn and patent information is matched which can be used as proxy variables of individual determinant factors by advanced researches. In addition, to validate whether the model proposed has a statistically meaningful effect, total 517 technology transactions are collected from both public and private technology transaction offices and analysed by multiple regression analysis, which led to significant patent determinant factors in deciding its value. As a result, it is herein presented that patent connectivity(number of literature cited) and commercialization stage in market influence significantly on patent valuation. The meaning of this study is in that it suggests the significant quantitative determinants of patent valuation based on the technology transactions data in practice, and if research results by industry are systematically verified through seamless collection of transaction data and their monitoring, we would propose the customized patent valuation model by industry which is applicable for both strategic planning of patent registration and achievement assessment of research projects (with representative patents).

• The Journal of the Korea Contents Association
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• 제20권8호
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• pp.572-582
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• 2020

The purpose of this study was to investigate workplace bullying, job stress, organizational commitment, and turnover intention as well as determine influencing factors on the turnover intention of small and medium-sized hospital nurses. Participants included 140 nurses from six small and medium-sized hospitals with less than 300 beds in G-city and J-province. Data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Pearson's correlation coefficient, and stepwise multiple regression, using the SPSS Win 21.0 program. The regression model was statistically significant (F=37.11, p<.001), and the explanatory power for turnover intention was 34.2%. The significant factors influencing turnover intention were organizational commitment (β=-.41, p<.001 and job stress (β=.25, p=.005). Human resources management is crucial for providing high quality healthcare service. The results of this study indicated that it is important to lower job stress and increase organizational commitment in order to reduce turnover intention among nurses of small and medium-sized hospitals. Based on these findings, customized programs for nurses in small and medium-sized hospitals need to be developed and implemented so as to lower their turnover intention and promote efficient management of healthcare human resources.

• Journal of Intelligence and Information Systems
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• 제27권1호
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Korean journal of applied entomology
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• 제61권4호
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• pp.563-575
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• 2022

Maruca vitrata is one of important pests in leguminous crops, especially red bean. We investigated the effects of temperature on development of each life stage, adult longevity and fecundity of M. vitrata for understanding the biological characteristics of the insect species at eight constant temperatures of 13, 16, 19, 22, 25, 28, 31, and 34℃. Eggs hatched successfully at all temperature subjected and larvae successfully developed to the adult stage from 16℃ to 31℃. The developmental period of egg decreased up to 31℃ and after then increased. The developmental period of larva and pupa, and adult longevity of M. vitrata decreased with increasing temperature. Lower and higher threshold temperature (TL and TH) were calculated by the Lobry-Rosso-Flandrois (LRF) and Sharpe-Schoolfield-Ikemoto (SSI) models. The lower developmental threshold (LDT) and thermal constant (K) from egg hatching to adult emergence of M. vitrata were estimated by linear regression as 12.8℃ and 280.8DD, respectively. TL and TH from egg hatching to adult emergence using SSI model were 14.2℃ and 31.9℃. Thermal windows, i.e., the range in temperature between the minimum and maximum rate of development, of M. vitrata was 17.7℃. In addition, we constructed the oviposition models of adult, using the investigated adult traits including survival, longevity, oviposition period and fecundity. Temperature-dependent development models and adult oviposition models will be helpful to understand the population dynamics of M vitrata and to establish the strategy of integrated pest management in legume crops.