• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.155-169
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• 2014

With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

• Women's Health Nursing
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• v.4 no.1
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• pp.161-178
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• 1998

In spite of the great progress of the theory and skill of the Nursing Care & Medical area in relation to pregnancy, nurses in clinics face up to many challenges in maternity nursing care areas. The reason is that the mobility and mortality of mothers was sharply decreased and the unknown high-risk diseases of pregnancy woman in the past is made public. That's why it is difficult to meet the pregnancy woman in natural process from pregnancy to delivery in recently. Admission rooms are filled with high-risk pregnancy women. As a matter of fact, we have done nursing care into the surface symptoms and diseases of high-risk pregnancy women so far. We have been indifferent to a long period hospitalization, separation from family, and conflict of repeated examination. Therefore, it is widely spread to understand the emotional conflict experienced by high-risk pregnancy women and to need for nursing intervention to bring up about emotional support and the ability of perception in psychological crisis. Although the pregnancy woman judged in high-risk should carry out normal task of pregnancy, she have to be confronted with secondary risk situation. The health of self & fetus threatened by the risk situation could be decreased through care plan, but psychological stress increases. Therefore, the pregnancy brings into non-control state. It is important to ask that what the hospitalized pregnancy women in high-risk think of themselves status. Because misunderstanding or serious anxiety of themselves status put into mother and fetus in danger. And adaptation mode makes all the difference. I would like to consider how nurses could deal with this high-risk circumstances in the position of pregnancy woman on the basis of the above fact. This study uses phenomenological method to suggest the basis material for nurses to do nursing intervention in view of pregnancy woman. Because this method understands the nature of true life of pregnancy woman throughly. The phenomenological method is the sources to describe or explain affluently the process generated in confirmation areas and environment and is the application for readers to understand and recognize clinic reality and then apply this method to reasoning study place or other places. Specifically, the phenomenon study method, one of the phenomenological method, is applied. The use of that method is to describe and generalize the experience in environment exactly. The study of this study is as follows : Among 187 descriptive stamens from 8 study participants are classified into 42 theme cluster at the stage of the first analysis. Those theme is categorized into 8 sub-subjects such as anxiety of uncertainty, foreknowledge about risk circumstance, will power about overcome, unsettled feeling about hospital, relief, optimistic thought, family support, and indifferences. At the last stage of analysis, those things are categorized into 3 subjects. When high-risk pregnancy woman foretell the situation, they feel unsettlement about uncertainty and untrust feeling about hospital. But they are ease with family support and hospital support. On the other hand, they express indifferent 3-way structure response to the situation having will of overcome and exceeding optimistic thought. In those statements, the experience by pregnancy woman shows 3 respect subjects. 1. They are anxious of this situation and are in desperation and don't recognize their role to be carried out 2. They think of this situation as normal process of pregnancy and are not concerned that this can give themselves and fetus fatal damage. 3. The pregnancy women will never confront this situation. This study shows the pregnancy woman has anxiety and optimistic relief about the situation, and ignores and optimistic relief about the situation, and ignores many things. Therefore, nurses in clinic should give pregnancy woman knowledge and information about the high-risk and help them to deal with the situation spontaneously. High-risk pregnancy woman should have the care plan in respect of the right perception. And the nurse know that their support help out pregnancy woman overcome the crisis in this respect of the special nursing intervention.

• Journal of Korean Philosophical Society
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• v.148
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• pp.239-267
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• 2018

In the matter of ethical and moral practice, Thomas Aquinas's thought is called "intellectualism". It does not mean only that intelligence is more important than will in moral practice, but that it has epistemological, metaphysical, and psycho-psychological implications significance. The first means affirming "the first principles of knowing" as the problem of certainty of knowing. In Thomism, there are surely above suspicion notions in the domain of practice as well as in the domain of reason, which are obviously self-evident, and because of that certainty, they become the basis of certainty of all other knowings that follow. The principle to know these knowings is the first principle of knowing, reason and Synderesis(conscience). Therefore, the "intellectualism" of Tomism is the basis for providing the ground of metaphysics. In the case of reason, it is classified into superior reason and inferior reason according to whether it is object. The object of higher reason is "metaphysical object" which human natural reason can not deal with. This affirmation of superior reason provides a basis for human "autonomy" in the moral and religious domain. This is because even in areas beyond the object of natural reason, it is possible to derive certain knowledge through self-reasoning, and thus to be able to carry out the act through their own choosing. Likewise, for Thomas Aquinas, "Synderesi" as the first principle of good and evil judgment can be applied to both the superior reason and the inferior reason, and thus, except for the truth by the direct divine revelation, precedes any authority of the world, scrupulous Act always guarantees truth and good. This means "subjectivity" that virtually in the act of moral practice, it can become the master of one's act. Furthermore, "consciousness(conscientia)", which means the ability to comprehend everything in a holistic and simultaneous manner, is based on conscience(synderesis). So, at least in principle, correct behavior or moral behavior in Tomism is given firstly in correct knowledge. Therefore, it can be said that true awareness (conscious awareness) in Thomas Aquinas's thought coincide with practical practice, or at least knowledge can be said to be a decisive 'driver' for practice. This will be the best explanation of the definition of "intellectualism" by Thomism.

• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.17-35
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• 2021

The purpose of this study is to derive implications for the development of the next home economics curriculum by comparing the 'function' and 'achievement standard' presented in the 14 subjects of the 2015 revised middle school common curriculum with the home economics curriculum. For this, keyword network analysis was conducted, and the results are as follows. First, in the 'function' of the 2015 revised middle school common curriculum, 'analysis, use, and expression' were found to be core function keywords with high Degree Centrality and the Eigenvector Centrality. Second, the functional keywords 'understanding, explanation, expression, analysis, and use' in the 'achievement standard' of the 2015 revised middle school common curriculum appeared with high frequency, and 'practice, problem-solving, search and reasoning' which are related to practical problem-solving ability appeared. It was confirmed that 'appreciation, solution and realization', which have relatively high Eigenvector Centrality, were core functional keywords used in the 'achievement standard'. Third, when the 'function' and 'achievement standard' of the 2015 revised middle school home economics curriculum were matched and compared, 7 out of 15 functions were not used in the statement of 'achievement standard', so the connection between 'function' and 'achievement standard' appeared to be insufficient. In addition, the diversity of functional keyword used in the 'achievement standard' was also found insufficient when compared to the middle school common curriculum. Therefore, this study propose strengthening the connectivity of 'function' and 'achievement standard' in the next home economics curriculum, using keywords such as 'analyze', 'express', 'compare', 'understand', 'interpret', 'explore', 'appreciate', and 'solve'.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.