• 한국간호교육학회지
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• 제14권1호
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• pp.55-62
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• 2008

Purpose: The purpose of this study was to compare and analyze learning satisfaction, critical thinking disposition, learning attitude and motivation between Problem Based Learning and Subjective Based Learning. Method: The research was performed between September and December, 2005 and 2006, including the development of PBL packages and their application. Statistical analysis was performed using SPSS 13.0. An independent t-test, $X^2$-test, and Pearson Correlation Coefficient were performed to compare the two groups on each of the measures. Result: There were no statistically significant differences among participants in the two groups according to general characteristics. However, The PBL group scored significantly higher on learning satisfaction, critical thinking disposition, learning attitude and motivation. Conclusion: This study contributes to our understanding of student outcomes of the PBL approach compared to the SBL approach. PBL needs to be extended over individual nursing courses for the unification of related courses and a curriculum.

• Smart Structures and Systems
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• 제29권1호
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of the Korean Data and Information Science Society
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• 제22권1호
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• pp.99-105
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• 2011

We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.

• ETRI Journal
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• 제37권3호
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.345-353
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• 2006

Over the past few years, training professionals have become more pragmatic in their approach to technology-based media by using it to augment traditional forms of training delivery, such as classroom instruction and text-based materials. This trend has led to the rise of the term blended learning. Blended learning, an environment of e-learning, is a powerful learning solution created through a mixture of face-to-face and online learning delivered through a mix of media and superior learning experiences. In this article we design and implement an e-learning environment for blended learning. The environment focused on following factors: learning activity and participation of learners, and real time feedback of instructor.

• 한국측량학회지
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• 제34권4호
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Communications for Statistical Applications and Methods
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• 제31권2호
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• pp.213-234
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• 2024

In recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be competitive for datasets with time-series data. Long short term memory (LSTM) further improves RNN by providing an alternative approach to the gradient loss problem. LSTM has its own advantage in predictive accuracy by retaining memory for a longer time. In this paper, we combine both supervised and unsupervised dimension reduction methods with LSTM to enhance the forecasting performance and refer to this as a dimension reduction based LSTM (DR-LSTM) approach. For a supervised dimension reduction method, we use methods such as sliced inverse regression (SIR), sparse SIR, and kernel SIR. Furthermore, principal component analysis (PCA), sparse PCA, and kernel PCA are used as unsupervised dimension reduction methods. Using datasets of real stock market index (S&P 500, STOXX Europe 600, and KOSPI), we present a comparative study on predictive accuracy between six DR-LSTM methods and time series modeling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권9호
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• pp.2495-2511
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• 2024

State of the art machine learning methods can enhance the analysis of clinical data and improve the ability to predict patient outcomes because data collected from clinical records, such as heart failure mortality studies, are often high dimensional, heterogeneous and give challenges to traditional statistical analysis techniques. To address this challenge, this study conducted a survival analysis based on a dataset of 299 patients with heart failure, using Python libraries. Cox regression was used to model and analyse mortality, and to find which features are strongly associated with this outcome. The Kaplan-Meier survival curve approach was used to show the patterns of patient survival over time. The analysis showed that age, ejection fraction, and serum creatinine level were significantly (p≤0.001) associated with mortality. Anaemia and creatinine phosphokinase also reached statistical significance (p-values 0.026 and 0.007, respectively). The Cox model showed good concordance (0.77) with the data, suggesting that the identified variables are useful for predicting mortality in patients with heart failure.

• 농촌지도와개발
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• 제8권1호
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• pp.25-40
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• 2001

The purposes of the study were to identify satisfaction and achievement of learning by female learner's characteristics, and to suggest measures to improve quality of education in internet education programs. In order to determine the educational effectiveness associated with the characteristics of learners, this study attempted to employ two way approaches by observing the degree of achievement for learning, which represents an instructor-oriented approach, and the degree of satisfaction for education, which represents a learner-oriented approach to enhance the quality of internet education for female learners. As an approach to evaluate the educational effectiveness, the degree of achievement in learning(Tyler's classical approach), and the degree of satisfaction for education (Scriven's consumer-oriented evaluation model) were utilized. A survey form was developed by the researcher after reviewing the various tools originated from Boshier, Cross, Gagne and Choi, and distributed to a panel of judges that examined the content validity of the instrument. The sample for the study consisted of 160 female learners from three universities in Seoul and capital area, and the survey form was used to collect data for this study. The SPSS WIN program was used in analyzing the data and a series of statistical tests were conducted including frequency, percentile, t-test, ANOVA, correlation, multiple regression, and factor analysis. The statistical significance level was 0.05. The following conclusion were drawn from this study of female internet education. First, it was evident that female internet learners tend to utilize information from internet, and this can be interpreted as participants' positive attitude, and voluntary participation. Second, educational facilities and services should be improved in the future, because the level of satisfaction was low in these areas compared to curriculum and educational methodology. Third, the participating factors influenced by the level of satisfaction for education of learner characteristics were the 'formation of inter-personal relationship and willingness to change' and the 'needs for education on internet', thus appeared that both social and educational needs influenced the level of satisfaction for education. Fourth, the degree of achievement in learning was higher in the order of 1) attitude 2) function 3) knowledge, thus, attitude change was the most important in achievement of learning. Fifth, the individual background that influenced the level of achievement in learning were age and educational experience. As for the individual level of achievement for learning, the younger and more educated group were more satisfied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.