• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.241-249
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• 2021

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.425-435
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• 2024

The purpose of this study was to identify factors influencing learning satisfaction of nursing students in online non-face-to-face classes during COVID-19 pandemic. 138 undergraduate nursing students were recruited from C nursing colleges of C city in Gyungnam. Data was collected using a self-reported online-questionnaire from 5 January to 18 January, 2022. Collected data were analyzed by SPSS/WIN 27.0 program using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression. Factor influencing learning satisfaction among undergraduate nursing students on online non-face-to-face classes during COVID-19 pandemic were teaching presence(presence of teacher)(𝛽=.43, p<.001), instructional quality(content qualities)(𝛽=.41, p<.001), Satisfaction of nursing major(satisfaction)(𝛽=.13, p<.001). instructional quality(interface)(𝛽=.12, p=.036), which explained about 85.3% of total variance(F=192.78, p<.001). Therefore, in order to improve class satisfaction in online non-face-to-face classes, it is necessary to operate classes that prioritize the presence of teacher so that learners can recognize and trust the presence of teacher by passionately professional performing. And, among instructional quality, we should be strengthen the usefulness of learning materials, content factors related to tests and assignments, and strive to improve satisfaction of nursing major. In addition, it is necessary to prepare and operate classes that fully consider the interface factors related to the manual composition and system convenience for online classes among the quality factors of classes.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.227-239
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• 2024

This study aims to analyze the impact of vocational training received by learning workers through the degree-linked work-study program on their learning outcomes. Specifically, we explore the causal relationship between various factors considered during university degree program admission and selection, and the average GPA (Grade Point Average) after admission. To achieve this, we conducted regression analysis and variance analysis using historical admission data and GPA records of 976 students from three undergraduate programs at a domestic K university that implements the degree-linked work-study model. Additionally, we included company information from publicly available databases that could potentially influence the academic performance of learning workers. Our analysis revealed significant causal relationships across various factors, including the classification of the high school attended, gender, family background, subject-specific grades in high school, duration of employment at the company, and age at the time of admission. Based on these findings, we anticipate that universities operating similar degree programs can enhance their selection procedures for learning workers. Furthermore, the results of this study can serve as foundational data for future policy recommendations related to degree-linked work-study programs.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.959-971
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• 2019

CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

• Journal of KIISE:Software and Applications
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• v.29 no.12
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• pp.966-978
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• 2002

There are two important problems in improving text classification systems based on machine learning approach. The first one, called "selection problem", is how to select a minimum number of informative documents from a given document collection. The second one, called "composition problem", is how to reorganize selected training documents so that they can fit an adopted learning method. The former problem is addressed in "active learning" algorithms, and the latter is discussed in "boosting" algorithms. This paper proposes a new learning method, called AdaBUS, which proactively solves the above problems in the context of Naive Bayes classification systems. The proposed method constructs more accurate classification hypothesis by increasing the valiance in "weak" hypotheses that determine the final classification hypothesis. Consequently, the proposed algorithm yields perturbation effect makes the boosting algorithm work properly. Through the empirical experiment using the Routers-21578 document collection, we show that the AdaBUS algorithm more significantly improves the Naive Bayes-based classification system than other conventional learning methodson system than other conventional learning methods

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1557-1565
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• 2014

The purpose of this study was to identify the factors influencing learning flow of nursing student. Design of this study was a descriptive research. The participants for this study were 283 nursing college students with four-years curriculum in D city from June 14th to June 24th, 2013. Data were analyzed using descriptive statistics, t-test, one way ANOVA, Pearson's correlation and multiple regression with SPSS 18.0 Program. There were significant correlations among self-leadership, academic self-efficacy, major satisfaction, and the learning flow. As a multiple regression analysis, factors that have an effect on learning flow were self-leadership, and academic self-efficacy. These factors explain 58.2% of the variance in learning flow. Self-leadership, academic self-efficacy, and learning flow relationship were positive relevant. To enhance learning flow ability for nursing students, it is necessary to develop training program and academic environment for increasing self-leadership and academic self-efficacy.

• Journal of the Korean Applied Science and Technology
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• v.38 no.2
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• pp.567-578
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• 2021

The purpose of this study was to identify the relationship between growth mindset, grit and self-directed learning ability of nursing students and investigate the factors influencing self-directed learning ability in online education. Data were collected through online surveys; subjects were consisted of 194 nursing students from J University in province C. Data were analyzed based on frequency, percentile, mean, Pearson's correlations, and multiple regressions. Self-directed learning ability, mindset, and grit in nursing students showed significant positive correlations with each other. Grit had the strongest influence on nursing students' self-directed learning abilities, followed by satisfaction for major studies, standard of living, interpersonal relations, and growth mindset; these factors accounted for 38.1% of the total variance in self-directed learning abilities among nursing students(Adj. R²=.381, p<.001). Overall self-directed learning ability can be improved by grit, growth mindset enhancement program.

• Fibers and Polymers
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• v.7 no.4
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• pp.344-351
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• 2006

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.

• Computers and Concrete
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• v.25 no.6
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.