• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1731_1732
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• 2009

This paper presents a stability analysis of networked control systems with packet dropouts. The packet dropouts are modeled as a linear function of the stochastic variable satisfying Bernoulli random binary distribution and weighted moving average (WMA). The observer based controller scheme is designed to exponentially mean square stabilize the NCS. Simulation results is provided to show the applicability of the proposed method.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.37-43
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• 2021

This study proposed an effective automatic classification technique to identify dropout patterns of university students, and based on this, an intelligent recommender system to prevent dropouts. To this end, 1) a data processing method to improve the performance of machine learning was proposed based on actual enrollment/dropout data of university students, and 2) performance comparison experiments were conducted using five types of machine learning algorithms. 3) As a result of the experiment, the proposed method showed superior performance in all algorithms compared to the baseline method. The precision rate of discrimination of enrolled students was measured to be up to 95.6% when using a Random Forest(RF), and the recall rate of dropout students was measured to be up to 80.0% when using Naive Bayes(NB). 4) Finally, based on the experimental results, a method for using a counseling recommender system to give priority to students who are likely to drop out was suggested. It was confirmed that reasonable decision-making can be conducted through convergence research that utilizes technologies in the IT field to solve the educational issues, and we plan to apply various artificial intelligence technologies through continuous research in the future.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.603-608
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• 2024

Nonresponse and missing values are caused by sample dropouts and avoidance of answers to surveys. In this case, problems with the possibility of information loss and biased reasoning arise, and a replacement of missing values with appropriate values is required. In this paper, as an alternative to missing values imputation, we compare several replacement methods, which use mean, linear regression, random forest, K-nearest neighbor, autoencoder and denoising autoencoder based on deep learning. These methods of imputing missing values are explained, and each method is compared by using continuous simulation data and real data. The comparison results confirm that in most cases, the performance of the random forest imputation method and the denoising autoencoder imputation method are better than the others.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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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 Society of Physical Medicine
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• v.17 no.2
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• pp.75-82
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• 2022

PURPOSE: This study examined the correlation between the pulmonary function and respiratory muscle strengthening training on an unstable support surface and a stable support surface in stroke patients. METHODS: The study subjects were 22 stroke patients undergoing central nervous system developmental rehabilitation treatment. After excluding six dropouts, eight people in the experimental group and eight people in the control groups were classified by random sampling. Both groups performed central nervous system developmental rehabilitation therapy and were provided a 10-minute break. The experimental group was provided with an unstable support surface using Togu, and the control group was trained to strengthen the respiratory muscle in a stable support surface. Respiratory muscle strengthening training was conducted three times per week for 20 minutes. Before and after each group of experiments, a nonparametric test Wilcoxon signed rank test, and a Mann Whitney U-test analysis were used to analyze the variations between the two groups. All statistical significance levels (α) were set at 0.05. RESULTS: Both groups showed increases in the pulmonary function but showed significant differences only in the experimental group. There was a significant difference in the peak expiratory flow between the two groups. CONCLUSION: Central nervous system development rehabilitation treatment for patients with an impaired nervous system and respiratory muscle strengthening training on unstable support surfaces are effective in improving the pulmonary function of stroke patients. Therefore, they are expected to be applied to physical therapy programs to help various functional activities.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.919-926
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• 2023

Aim(s): This study aims to explore the relationship between multicultural awareness, multicultural acceptance and sensitivity of nursing students in the midst of rapid changes in multiculturalism, and to explore the direction for improving multicultural awareness as health care workers in the future. A survey was conducted among 135 nursing students from two universities in one region, and 108 students, excluding random responses and dropouts, were the final subjects for analysis. For data analysis, frequency analysis, correlation analysis, reliability analysis and mediation effects were tested using SPSS and process Macros. The results confirmed a statistically significant relationship between multicultural awareness and multicultural acceptance (r=.572, p<.001). The relationship between mutual cultural sensitivity, multicultural acceptance (r=.650, p<.001) and multicultural awareness (r=.456, p<.001) also showed a significant positive correlation. In addition, the effect of mutual cultural sensitivity was confirmed in the relationship between multicultural awareness and multicultural acceptance. As a result, in the relationship between multicultural awareness and multicultural acceptability, intercultural sensitivity ranged from 0.188 to 0.554, and the 95% confidence interval did not include 0; thus, indirect effect was statistically significant. Considering these results, it was confirmed that it is important to increase multicultural awareness and intercultural sensitivity in order to increase multicultural acceptance.