• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.667-684
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• 2022

Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying difficulties with current gene selection methods as well as future research directions are discussed.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1394-1402
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• 2023

This study is a descriptive research study to conducted the mediating effect of learning flow in the relationship between optimism and career adaptability in order to improve the career adaptability of nursing students. The subjects were students enrolled in the nursing department of 4-year universities in G and M cities. Data were collected from April to May 2023. The collected data were analyzed by descriptive statistics, Pearson's correlation coefficient, and Baron and Kenny's regression analysis. As a result of the study, first, optimism showed a positive effect on learning flow. Second, optimism showed a positive effect on career adaptability. Third, learning flow showed a partial mediating effect on the relation between optimism and career adaptability. From this, these findings suggest that strategies to promote a optimism and learning flow should be operated in order to improve the career adaptability of nursing students in the nursing education field.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.83-88
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• 2019

This study examines the effects of student-centered project-based learning for the development of creative problem-solving skills, communication skills, critical thinking skills, and cooperation. A college students' creative personality test was used and pre-and post-test were performed. and TOEIC Speaking practice test by Educational Testing Service were selected to measure the English communication skills. The SPSS 18.0 was used and validated at a significance level of 5%. The result of this study shows that in the case of 'independence', the post-test average of the experimental group was statistically significant at the significant level (p<.01), which also showed statistically significant difference. There was statistically significant difference between the control group ($M=127{\pm}08.2$) and in the experimental group ($M=132{\pm}18.7$) applying project-based and problem-based convergent learning to English class were positively changed.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.91-102
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• 2019

In autonomous driving systems, the ability to classify pedestrians in images captured by cameras is very important for pedestrian safety. In the past, after extracting features of pedestrians with HOG(Histogram of Oriented Gradients) or SIFT(Scale-Invariant Feature Transform), people classified them using SVM(Support Vector Machine). However, extracting pedestrian characteristics in such a handcrafted manner has many limitations. Therefore, this paper proposes a method to classify pedestrians reliably and effectively using CNN's(Convolutional Neural Network) deep features and transfer learning. We have experimented with both the fixed feature extractor and the fine-tuning methods, which are two representative transfer learning techniques. Particularly, in the fine-tuning method, we have added a new scheme, called M-Fine(Modified Fine-tuning), which divideslayers into transferred parts and non-transferred parts in three different sizes, and adjusts weights only for layers belonging to non-transferred parts. Experiments on INRIA Person data set with five CNN models(VGGNet, DenseNet, Inception V3, Xception, and MobileNet) showed that CNN's deep features perform better than handcrafted features such as HOG and SIFT, and that the accuracy of Xception (threshold = 0.5) isthe highest at 99.61%. MobileNet, which achieved similar performance to Xception and learned 80% fewer parameters, was the best in terms of efficiency. Among the three transfer learning schemes tested above, the performance of the fine-tuning method was the best. The performance of the M-Fine method was comparable to or slightly lower than that of the fine-tuningmethod, but higher than that of the fixed feature extractor method.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.308-316
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• 2021

During the past two years, the education systems in the world witnessed unprecedented turmoil due to the coronavirus (Covid-19) pandemic, as most schools and universities in the world closed their doors to more than 1.5 billion students, or more than 90% of the total learners, according to recent figures issued by the UNESCO Institute for Statistics. Education experts have agreed that post- coronavirus education will not be the same as before, especially with the increasing use of modern technology in education. One of the most important new patterns with a structure digital in education is distance education, this style has been used, in many countries of the world, as an alternative to traditional education, since the beginning of the pandemic. In Saudi Arabia, this type of education has been used in all educational institutions, starting from kindergarten until the postgraduate level, as an alternative to face-to-face education to preserve the health and safety of students and workers in educational institutions. This study aimed to explore the level of satisfaction of preparatory year students on distance learning in their first year of study at Umm Al-Qura University. The findings of this study showed that students in the preparatory year were satisfied with their online learning experience. In addition, the results revealed that there was no effect for gender and location of study on students' level of satisfaction. Saudi universities should continue to work to create a suitable learning environment for students at the e-learning level.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Journal of the Korean School Mathematics Society
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• v.22 no.4
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• pp.395-413
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• 2019

The purpose of this study was to find out how Blended e-Learning affected the teaching and learning of engineering mathematics for engineering students. It has researched the application condition of Blended e-Learning and the students' attitude in the offline classes of students. The subject were 42 students of Junior in the Department of Mechanic Engineering in M-University participated in the study. The lecturer taught the class for the students by fact-to-face teaching at the offline. It was recorded all processes during the class, and the video was loaded at the Learning Management System(LMS). The students studied online by themselves. This study investigated the attitude of students at the offline and the Utilization of Online Data by learners through the mixed class for one semester. The results were as follows. First, Blended e-Learning applied engineering mathematics affected positively for the self-regulated and individualized learning to the students. Second, Blended e-Learning has shown a positive impact on the teaching and learning of engineering mathematics. Finally, it also had a positive effect on the class satisfaction level of students.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3436-3443
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• 2012

The purpose of this study was to examine the impact of a sense of classroom community and social presence on learning satisfaction in a cyber learning setting. The subjects in this study were 172 sixth graders in M elementary school in the city of D, who studied in a cyber setting at home. A survey was conducted to gather data, and multiple regression analysis were carried out to determine the influence of a sense of classroom community and social presence on learning satisfaction. As a result, it is found that a sense of classroom community and social presence had a significant correlation to learning satisfaction. A sense of classroom community turned out to affect learning satisfaction. A sense of classroom community are a major variable that should seriously be taken into account in an elementary cyber learning setting in order to boost the learning satisfaction of learners. In the future, the kinds of instructional design that could foster a sense of classroom community is required when cyber learning environments are prepared.