• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.24 no.3
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• pp.55-64
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• 2011

Objectives : This study was performed in order to investigate the effectiveness of electroneuronography as predicting factors for peripheral facial palsy prognosis. Methods : Data were gathered with 32 Bell's palsy patients. The grade of Bell's palsy were measured 2 times; first medical exam and 4 weeks after treatment, with Lucille Daniels's Muscle test. We converted the grade system use on five rating scale. The significance of treatment verified with paired t-test used on first medical exam and 4 weeks after treatment score and predicting factors of electroneuronography verified with simple regression test. Results : The improvement score were statistically significant different before and after treatment(p<.001). Mean axonal loss according to electroneuronography showed a statistically significant correlation in predicting peripheral facial palsy (P<0.01). Conclusion : Axonal loss as determined by electroneuronography has statistical significance as predicting factors for peripheral facial palsy prognosis.

• Journal of the Korean Society of Safety
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• v.19 no.3 s.67
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• pp.89-94
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• 2004

Most reinforcements in concrete are constructed by steel. Corrosion of reinforcement is the main cause of damage and early failure of reinforced concrete structures. The corrosion is mainly professed by the chloride ingress. In general, chloride in concrete can be discriminated by two components, total chloride and fire chloride. This paper provides a testing method on the coefficient of chloride diffusion in concrete and the relationship between total chloride and free chloride in concrete for the composition of predicting model on diffusion rate of chloride. In order to complete this predicting model, this study will use chloride penetration characteristic, diffusion coefficient and experiment of color change on silver nitrate solution. This predicting model is going to help that grasp special quality on salt content inclusion of concrete structure that is exposed in chloride environment. Accurate predicting model can be effectively used not only in selecting of repair time but also in preventing from various deteriorations.

• Journal of Korean Academy of Nursing
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• v.4 no.3
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• pp.57-62
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• 1974

This study was made to assess the possibility of predicting the nurses efficiency after graduation in college through their entrance examination and general attitude test results and their academic records. It was observed that generally nurses with the highest academic records were the most efficient in their work after graduation while those with high general aptitude test results came next although their college entrance examination results were comparatively low. Therefore it could be deduced from this study that college entrance examination results alone could not be depended upon in predicting the nurse's working efficiency after bet graduation.

• Genomics & Informatics
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• v.8 no.4
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• pp.212-218
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• 2010

An accurate system for predicting the survival of patients with oral squamous cell carcinoma (OSCC) will be useful for selecting appropriate therapies. A nomogram for predicting survival was constructed from 96 patients with primary OSCC who underwent surgical resection between January 1994 and June 2003 at the Yonsei Dental Hospital in Seoul, Korea. We performed univariate and multivariate Cox regression to identify survival prognostic factors. For the early stage patients group, the nomogram was able to predict the 5 and 10 year survival from OSCC with a concordance index of 0.72. The total point assigned by the nomogram was a significant factor for predicting survival. This nomogram was able to accurately predict the survival after treatment of an individual patient with OSCC and may have practical utility for deciding adjuvant treatment.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.402-409
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• 2007

Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.167-175
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• 2020

The net income per unit area from crop production could be the most critical consideration for agricultural producers during cultivation planning. This paper proposes a scheme for predicting the net income per unit area based on machine learning and related calculations. This scheme predicts rice production and operation costs by applying climate and price index data. The rice price is also predicted by applying rice production and operation cost data. Finally, these predicted results are employed to calculate the predicted net income, which is compared with the actual net income. Consequently, the proposed scheme shows a meaningful degree of conformity, which indicates the potential of machine learning for predicting various aspects of agricultural production.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.111-116
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• 2017

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.1
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• pp.135-142
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• 1997

The objectives of this study was to test applicability of the theory of planned behavior by Ajzen & Driver(1992), to predicting summer vacation activity participation. Vacations to vist Sock-Cho City in summer were asked to complete a questionaire to measure attitudes, subjective norms, perceived behavioral control, and intentions in relation to participating in three vacation activities, at the beach, at the valley, and at the pool. After summer vacation, respondents were called to answer whether or not they had participated in those activities. The results showed that attitudes toward vocation consist of affective and instrumental component Consistent with the theory, attitudes, subjective norms and perceived behavioral control were significant variables in predicting intentions to participate in vacation activites. In addition, intentions and perceived control were influential in predicting vacation activities partication. Althouh the objectives of the study were achived, this application of the theory of planned behavior to Koreans summer vacation participations did not show the same power as the Ajzen & Driver's study(1992) in predicting recreation activties participation. It would be desirable for future research to apply the theory of planned behavior to various recreational settings.

• Earthquakes and Structures
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• v.25 no.1
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• pp.15-26
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• 2023

It is of great importance to assess the residual displacement demand in the performance-based seismic design. In this paper, a hybrid deep learning model for predicting the residual displacement spectra under near-fault (NF) ground motions is proposed by combining the long short-term memory network (LSTM) and back-propagation (BP) network. The model is featured by its capacity of predicting the residual displacement spectrum under a given NF ground motion while considering the effects of structural parameters. To construct this model, 315 natural and artificial NF ground motions were employed to compute the residual displacement spectra through elastoplastic time history analysis considering different structural parameters. Based on the resulted dataset with a total of 9,450 samples, the proposed model was finally trained and tested. The results show that the proposed model has a satisfactory accuracy as well as a high efficiency in predicting residual displacement spectra under given NF ground motions while considering the impacts of structural parameters.

• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.