• 한국응용과학기술학회지
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• 제40권1호
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• pp.113-122
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• 2023

본 연구는 미용 전공 대학생의 비대면 수업 경험이 학습몰입에 미치는 영향에 대해 알아보고 With Corona 시대에 맞는 미용 교육산업에 기초자료를 제공하기 위함에 목적이 있다. 미용 전공 대학생 300명을 대상으로 2022년 06월 07일부터 06월 21일까지 자가기입식 설문법을 실시하였으며 총 286부를 표본으로 실증 분석하였다. SPSS ver. 21.0 프로그램을 활용하여 빈도분석, 요인분석, 탐색적 요인분석, 기술통계분석, 상관관계분석, 다중회귀분석법을 사용하여 연구하였다. 그 결과, 비대면 학습경험을 요인 분석한 결과 수업만족 2개의 하위요인으로 분석되었으며 학습몰입을 요인 분석한 결과 학습 즐거움과 학습몰입의 2개의 하위요인으로 분석되었다. 수업 활동이 학습몰입의 하위요인인 학습 즐거움(𝛽=.279, p<.007)과 학습몰입(𝛽=.221, p<.031)에 통계적으로 유의미한 정(+)의 영향을 미치는 것으로 나타났다(p<.05). 수업 만족이 학습몰입의 하위요인인 학습몰입(𝛽=.223, p<.041)에 통계적으로 유의미한 정(+)의 영향을 미치는 것으로 나타났다(p<.05). 본 연구결과를 토대로 비대면 수업 경험이 학습몰입에 영향을 미친다는 사실을 알 수 있었으며 이를 통해 시대에 맞는 효율적 비대면 교육 방안이 활발히 모색되기를 기대하는 바이다.

• Journal of the Korean Data and Information Science Society
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• 제12권2호
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• pp.83-93
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• 2001

We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

• International Journal of Internet, Broadcasting and Communication
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• 제15권1호
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• pp.157-163
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• 2023

Recently, as businesses and data types become more complex and diverse, efficient data analysis using machine learning is required. However, since communication in the cloud environment is greatly affected by network latency, data analysis is not smooth if information delay occurs. In this paper, SPT (Safe Proper Time) was applied to the cluster-based machine learning data analysis agent proposed in previous studies to solve this delay problem. SPT is a method of remotely and directly accessing memory to a cluster that processes data between layers, effectively improving data transfer speed and ensuring timeliness and reliability of data transfer.

• 기본간호학회지
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• 제26권1호
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• pp.12-22
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• 2019

Purpose: This study was done to verify the self-directed learning instrument (SDLI) developed to measure self-directed learning ability in nursing students. Methods: The participants for the study were 425 nursing college students. Their self-directed learning was verified using self-reports and results through questionnaires. SDLI was translated into Korean through translation/reverse translation process and its content validity verified by five experts. The validity of the instrument was verified through item analysis, exploratory factor analysis, and confirmatory factor analysis. Reliability verification was analyzed using internal consistency reliability. Results: Four factors were identified through exploratory factor analysis and 20 items of the original instrument were found to be valid. In the confirmatory factor analysis, the validity of the instrument was verified as the model was valid. The internal consistency reliability was also acceptable and SDLI was found to be an applicable instrument. Conclusion: SDLI has been developed and verified by selecting nursing students as participants for the study. Use if SDLI is expected to improve the quality of self-directed learning in nursing education and to be used in future nursing research.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.169-180
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• 2021

Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.

• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.39-46
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• 2023

The purpose of this study is to introduce a proposed Framework for Evaluating the Return on Investment (ROI) of E-Learning Programs at Saudi Universities. To achieve this goal, the descriptive analysis methodology is used to analyze the literature review about e-learning and its evaluation from different viewpoints, especially from the ROI-related perspective. As well as the literature reviews related to ROI and the methods of calculating it inside society institutes. This study suggests a conceptual framework for evaluating the ROI of E-Learning Programs at Saudi Universities. This framework is based on the merging process among the analyze, design, develop, implement, and evaluate (ADDIE) model for designing e-learning programs, which gives detailed procedures for executing the program, several evaluating models for e-learning, and the Kirkpatrick model for evaluating the ROI of e-learning. It consists of seven stages (analysis, calculating the costs, design, development, implementation, calculation of the benefits, and calculation of the final ROI).

• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.164-170
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• 2023

With the recent advances in AI (artificial intelligence) and HPC (high-performance computing) technologies, deep learning is proliferated in various domains of the 4th industrial revolution. As the workload volume of deep learning increasingly grows, analyzing the memory reference characteristics becomes important. In this article, we analyze the memory reference traces of deep learning workloads in comparison with traditional workloads specially focusing on read and write operations. Based on our analysis, we observe some unique characteristics of deep learning memory references that are quite different from traditional workloads. First, when comparing instruction and data references, instruction reference accounts for a little portion in deep learning workloads. Second, when comparing read and write, write reference accounts for a majority of memory references, which is also different from traditional workloads. Third, although write references are dominant, it exhibits low reference skewness compared to traditional workloads. Specifically, the skew factor of write references is small compared to traditional workloads. We expect that the analysis performed in this article will be helpful in efficiently designing memory management systems for deep learning workloads.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.