• Journal of Korean Home Economics Education Association
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• v.19 no.1 s.43
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• pp.99-114
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• 2007

This study aims at investigating how middle school students perceive the content of the 'Me and My Family Relations' unit in the technology and home economics textbook, act on such perceptions and how this connects with their healthy family relations. In addition, the study also points at inquiring into what kind of differences and mutual influences can be found in the above-mentioned three factors according to family environment. With this objective. this research has analyzed survey data conducted on 401 7th grade middle school students residing in Incheon Greater city, collected by the random sampling method. The findings are as follow: First, the students were found to have positive perceptions on the 'family relations and communication' unit in the technology and home economics text book. However. they were also found to perceive that the content was not as realizable in their everyday family lives. Second, the number of students who perceived their family lives to be healthy was found to be quite high. The students perceived their family lives to be healthy projecting from such aspects as the degree of gratification and affection, extent of family bonding, communication patterns, and problem solving abilities, in the same order. In addition, the higher the families' socio-economic level, and in the cases that the students had working mothers and the fathers held higher degrees, the degree to which the families were perceived to be healthy was higher. Third, in investigating the influence that such factors as the students' family environment, the degree that students perceived the text book content positively, and the degree that the students perceive the content to be realizable have on healthy family relations, among these factors, the students' perceived degree of how healthy their family relations are had the most bearing over the above-mentioned factors. The second influential factor on how healthy family relations are was the family's affectional environment, found to be more influential than such factors as family type, the mother's employment status, living standards, and the parents' educational level. On the other hand, the perceived level of realizability was found to have a lower influence on the students' family relations than the perceived positivity.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.95-112
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• 2020

This paper is to investigate how cognitive bias of college students and entrepreneurs relates to perceived risks and entrepreneurial opportunities that represent uncertainty, and how various cognitive bias and entrepreneurial efficacy In the same way. The purpose of this study is to find improvement points of entrepreneurship education for college students and to suggest problems and improvement possibilities in the decision making process of current entrepreneurs. This empirical study is a necessary to improve the decision-making of individuals who want to start a business at the time when various attempts are made to activate the start-up business and increase the sustainability of the existing SME management. And understanding of the difference in opportunity evaluation, and suggests that it is necessary to provide good opportunities together with the upbringing of entrepreneurs. In order to achieve the purpose of the study, questionnaires were conducted for college students and entrepreneurs. A total of 363 questionnaire data were obtained and demonstrated through structural equation modeling. This study confirms that there is some relationship between perceived risk and cognitive bias. Overconfidence and control illusions among cognitive bias have a significant relationship between perceived risk and wealth. Especially, it is confirmed that control illusion of college students has a significant relationship with perceived risk. Second, cognitive bias demonstrated some significant relationship with opportunity evaluation. Although we did not find evidence that excess self-confidence is related to opportunity evaluation, we have verified that control illusions and current status bias are related to opportunity evaluation. Control illusions were significant in both college students and entrepreneurs. Third, perceived risk has a negative relationship with opportunity evaluation. All students, regardless of whether they are college students or entrepreneurs, judge opportunities positively if they perceive low risk. Fourth, it can be seen from the college students 'group that entrepreneurial efficacy has a moderating effect between perceived risk and opportunity evaluation, but no significant results were found in the entrepreneurs' group. Fifth, the college students and entrepreneurs have different cognitive bias, and they have proved that there is a different relationship between entrepreneurial opportunity evaluation and perceived risk. On the whole, there are various cognitive biases that are caused by time pressure or stress on college students and entrepreneurs who have to make judgments in uncertain opportunities, and in this respect, they can improve their judgment in the future. At the same time, university students can have a positive view of new opportunities based on high entrepreneurial efficacy, but if they fully understand the intrinsic risks of entrepreneurship through entrepreneurial education and fully understand the cognitive bias present in direct entrepreneurial experience, You will get a better opportunity assessment. This study has limitations in that it is based on the fact that university students and entrepreneurs are integrated, and that the survey respondents are selected by the limited random sampling method. It is necessary to conduct more systematic research based on more faithful data in the absence of the accumulation of entrepreneurial research data. Second, the translation tools used in the previous studies were translated and the meaning of the measurement tools might not be conveyed due to language differences. Therefore, it is necessary to construct a more precise scale for the accuracy of the study. Finally, complementary research should be done to identify what competitive opportunities are and what opportunities are appropriate for entrepreneurs.

• The Korean Society of Law and Medicine
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• v.19 no.2
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• pp.41-72
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• 2018

In the division of labor (or teamwork) in medicine, the responsibility of medical and nursing staff should be separated or distributed to justify negligent criminal offenses. The present work refers to the standards by which the due diligence and responsibility of the individual persons are to be determined and delimited. In this context, it has been proven that objective theory as a measure of due diligence is appropriate. From a moral point of view, when assessing due diligence, it makes sense to impose greater individual or higher performance demands on the perpetrator, but law and order require that due diligence should result from socially relevant human behavior. To give objective measure of negligence and to provide the highest level of personal responsibility, so that man can not be burdened too much responsibility and it is accordingly with an equality theorem. Afterwards some points are presented, which should be considered in a concrete fact in the determination of the medical negligence. Medical action has specific characteristics such as professionalism, discretionary and exclusive, unbalance of information. These characteristics distinguish medical actions from general negligence. The general level of knowledge, the urgency, working condition and working environment of the medical facility, duration of the professional practice, assessment of the medical activity are crucial in this context. As a standard of delineation of due diligence, I have used the permitted risk and the principle of trust. In the horizontal division of labor, the principle of trust applies. The principle of trust applies in principle in cases of division of labor interaction, when doctors in the same hospital exercise their own specific occupational field or everyone works in another hospital. However, this is not true for every case. In the vertical division of labor, the principle of trust does not apply and the senior physician can not trust the assistant doctors. In this case, the principle of trust is converted into a duty of supervision for assistant doctors by the senior physician. This supervision requirement could be used as a random check.