• Journal of Veterinary Clinics
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• v.34 no.2
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• pp.140-145
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• 2017

The goal of this study was to investigate risk factors associated with porcine reproductive and respiratory syndrome (PRRS) in pig farms in the Republic of Korea using logistic regression and a multilevel model. A cross-sectional study was applied to 305 pig farms with a questionnaire-based interview by veterinarians between March 2014 and February 2015. The questionnaire comprised eight categories: proximity to neighbors, disinfection, visitors, vehicles, insecticides, wild animals, gilts, and feeding. In total, 61 questions in eight categories related to pig farm biosecurity were investigated. Farms were classified as PRRS stable or unstable based on the results of an antibody test and PCR. For univariate analysis, keeping production records with computers (OR = 0.283, 95% CI = 0.056 - 1.425), accredited farm with no use of antibiotics (OR = 0.412, 95% CI = 0.134 - 1.269), reviewing health record of semen prior to purchasing (OR = 0.492, 95% CI = 0.152 - 1.589), complete isolation of runt pigs (OR = 0.264, 95% CI = 0.084 - 0.829), compulsory registering for visitors (OR = 0.424, 95% CI = 0.111 - 1.612), keeping records of insecticide history (OR = 0.406, 95% CI = 0.089 - 1.846), routine on-farm monitoring by veterinarians (OR = 0.314, 95% CI = 0.069 - 1.423), and use of on-farm checklist for biosecurity monitoring (OR = 0.313, 95% CI = 0.063 - 1.553) were found to decrease the probability of PRRS infection. Multivariate and multilevel analysis revealed only two factors, complete isolation of runt pigs (OR = 0.165, 95% CI = 0.045 - 0.602 and OR = 0.208, 95% CI = 0.055 - 0.782) and compulsory registering for visitors (OR = 0.106, 95% CI = 0.017 - 0.655 and OR = 0.119, 95% CI = 0.017 - 0.809) were found to decrease the probability of PRRS infection. The intracluster correlation coefficient of a province for multilevel model was 0.05. The results of this study might facilitate biosecurity measures for individual farms to reduce the probability of PRRS infection.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• The Journal of Korean Philosophical History
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• no.52
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• pp.393-424
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• 2017

This study investigates the relationship between South Korean youths' Confucian values and sense of citizen ethics while presenting outlook on the sense of citizen ethics based on the theory of human nature. The purpose of this study, by doing so, is to present educational measures. For this purpose, empirical research method was applied in this study. In the empirical study, youths were surveyed and the answers were statistically analyzed and discussed with a view to achieve the study purpose. In the empirical research part of the study, Korean youths' awareness on Confucian values was examined along with its relationship with the sense of citizen ethics. The effect of Confucian values on sense of citizen ethics and their relationship were analyzed to evaluate the receptivity of youths on Confucian ideas and usefulness of sense of citizen ethics. This study investigated a total of final 311 sets of data from male and female students at middle and high schools located in Seoul, Gyeonggi, South Korea. First, to identify the youths' Confucian values and level of sense of citizen ethics, descriptive statistical analysis was conducted. As a result, the survey subjects were found to have, concerning the Confucian values, world view M=3.54, human relations view M=3.66, morality cultivation M=3.76, and social order M=3.45, higher than 3.0 to represent positive levels. The morality cultivation, in particular, was recorded the highest among all whereas the social order was relatively lower, which represents the degree of relying on Confucian values to establish social order. Second, the sub-variables of Confucian values were verified according to the personal characteristics of the surveyed youths and differences in their entire perception was investigated. As a result, according to gender, morality cultivation was found higher in female students (M=3.85) than in male students (M=3.64). According to the subjective economic level of their household, world view was found higher in upper class (M=3.98) than middle-low class (M=3.25) and low class (M=3.22) while human relations view was found higher in middle-upper class (M=3.79) than low class (M=3.46). As for the family type, morality cultivation was found higher in extended family (M=3.83) than nuclear family (M=3.62); and social order was higher in extended family (M=3.54) than nuclear family (M=3.36). Third, to verify the study theme of identifying the effects of youths' Confucian values on sense of citizen morality, hierarchical regression analysis was employed in this study, which used the multi-level model of multiple regression analysis. As a result, the Confucian values was found to have significant positive (+) correlations with the entire sense of citizen ethics in order of human relations view(${\beta}=.499$), world view(${\beta}=.412$), social order(${\beta}=.341$), and morality cultivation(${\beta}=.241$). Confucian value showed significant positive (+) correlations with autonomy in order of morality cultivation(${\beta}=.458$), human relations view(${\beta}=.454$), social order(${\beta}=.362$), and world view(${\beta}=.158$). Confucian values was found to have significant positive (+) correlations with community spirit in order of human relations view(${\beta}=.295$), social order(${\beta}=.281$), and morality cultivation(${\beta}=.232$). As shown in the findings above, youths' Confucian values was found to have significant positive (+) effects on the sense of citizen ethics. It is noted that the higher the Confucian values, the more positive the sense of citizen ethics would be. Consequentially, the Confucian values was identified to play an important role in the sense of citizen ethics in the modern society. Based on this analysis, this study presented specific measures - the necessity and possibility of education on sense of citizen ethics under the theory of human nature. To this end, this study proposed to find an optimal interface between the contemporary sense of citizen ethics and Confucian ethics through the respect for human life and nature, man of virtue as the ideal human model, and united society as a desirable society model.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.1-14
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• 2021

Innovation and Scale-up of Start-up companies are becoming important national tasks. In the past, it was spread the start-up policy paradigm such as 'Start-up America', 'Start-up Chile', 'Start-up Britain' to overcome the recession globally. However as the economic recovery has become more visible recently in advanced economies, it is shifting from a start-up support policy to a scale-up oriented policy paradigm such as 'Scale-up America', Scale-up UK', 'Scale-up Denmark'. It is necessary to enter the scale-up phase beyond the start-up phase to increase the number of high-quality jobs and to continue economic growth. Therefore, it is necessary to grow the start-up into a strong medium-sized company and to lay the foundation for survival. Therefore, the purpose of this study is to consider the antecedent factors that influence the scale-up aspiration for the start-up firm to grow into a scale-up company, and empirically identifies the differences between the stages of economic development and entrepreneurs in the country. In order to accomplish the purpose, this study predicted scale-up by aspiration which is a predictor of scale-up behavior because it is difficult to achieve visible growth in a short period of time due to the characteristics of start-up companies. In order to empirically explore these relationships, the data were collected from nascent entrepreneurs who have less than 3.5 years of the Adult Population Survey(APS) among the subjects surveyed by the Global Entrepreneurship Monitor(GEM) and the national economic development stage are divided into Innovation-driven, Efficiency-driven, Factor-driven type economies. For the test hypotheses, this study adopted the multi-level model analysis for comparison between national economic development stages and using the R 3.5.0 program. The results of this study are as follows. There is difference between the national economic development and the entrepreneur in the relationship between innovation orientation of entrepreneurs and scale-up aspirations. As the economy of the country develops, the innovation activity of the entrepreneur becomes more active. Since start-ups are heavily influenced by entrepreneurs, there is a difference in the degree of aspiration depending on how innovative an entrepreneur is in the same environment. In terms of the relationship between innovation orientation and scale-up aspiration, the fear of failure was found to differ between national economic development and entrepreneurs. The fear of failure differ from country to country, and this is one of the important factors affecting entrepreneurial activities. It is expected that the factors influencing the growth of the start-up companies which are identified through the results of these studies, will be used to create a suitable scale-up ecosystem according to the national economic development stage.