• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.179-200
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• 2015

Recent national policies National Competency Standards(NCS) to develop teaching-oriented education in the field of industry and learning is taking place. Plan to take advantage of the Internet and multimedia classes, information and communication technology (ICT) for ways to leverage the integration appearing in various forms. The purpose of this study is causal influence on the ARCS motivation theory can determine the basic psychology of human motivation factors and the desires of a typical human nature theory dealing with the psychological needs of interactivity and immersion is learning achievement and learning transfer and to validate the demonstration. By applying information and communication technology sector in the development of learning in information and communication equipment training program modules from a field study conducted at the NCS with a clear empirical and empirical research through the synchronization to the learner and to explore the possibility of generalization.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.57-65
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• 2020

At present, the existing virus recognition systems usually use signature approach to detect malicious executable files, but these methods often fail to detect new and invisible malware. At the same time, some methods try to use more general features to detect malware, and achieve some success. Moreover, machine learning-based approaches are applied to detect malware, which depend on features extracted from malicious codes. However, the different distribution of features oftraining and testing datasets also impacts the effectiveness of the detection models. And the generation oflabeled datasets need to spend a significant amount time, which degrades the performance of the learning method. In this paper, we use transfer learning to detect new and previously unseen malware. We first extract the features of Portable Executable (PE) files, then combine transfer learning training model with KNN approachto detect the new and unseen malware. We also evaluate the detection performance of a classifier in terms of precision, recall, F1, and so on. The experimental results demonstrate that proposed method with high detection rates andcan be anticipated to carry out as well in the real-world environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.849-859
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• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• The Journal of Korean Association of Computer Education
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• v.20 no.6
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• pp.61-69
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• 2017

The Goal of SW education is to improve computational thinking. Especially, non computer majors need to apply computational thinking to their problem solving in their fields after computational thinking class. In this paper, we verified what factors affect the improvement of computational thinking through mixed research method after teaching computational thinking to non major students. Also, we analysed the characteristics of initial learning transfer of computational thinking, and establish the reason about he validity and justification for non major in SW education. The result shows learning satisfaction, learning transfer motivation, and self-CT efficacy affect the perception about improvement of computational thinking. Also, we found that there is application of computational thinking was coming up with problem solving process because the initial learning transfer process of computational thinking has characteristics about concepts and practices of it in programming steps. The effectiveness and learning transfer process of computational thinking for non majors will give the validity and justification to teach SW education for all students.

• Industry Promotion Research
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• v.2 no.2
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• pp.31-44
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• 2017

This study is to investigate the relationship between the communication type recognized by members of organization and the learning transfer climate in the organization, and when it comes to the type of communications within organization set as independent variable, four variables such as communication with supervisor, media quality communication, and organizational outlook communication were proposed and regarding learning transfer climate which is a dependent variable, four sub-parameters such as supervisor support, colleague support, transfer opportunity and organizational compensation recognition were selected. 116 copies of the statistical data excluding statistically insignificant surveys were used for 150 participants who participated in the 2015 core competency curriculum for SMEs and 3 months have passed, in order to achieve this study's objective. Meanwhile, the research hypothesis was verified with the collected data through frequency analysis, factor analysis, reliability verification, technical statistical analysis, and simple and multiple regression analysis by using the statistical package program of SPSS 20.0. Results of this study have shown that firstly, communication with supervisor, media quality communication, and organizational outlook communication all showed a positive (+) significant influence on the superior support in the influence relationship between communication type in organization and supervisor support which is learning transfer climate. Secondly, communication type in organization doesn't appear to affect colleague support of learning transfer climate. Thirdly, communication with supervisor, media quality communication, and organizational outlook except for communication with colleagues have shown a positive (+) influence on transfer opportunity in the influence relationship between communication type in organization and transfer opportunity of learning transfer climate. Lastly, communication with supervisor and communication on organizational outlook showed positive(+) influence in the influence relationship between communication type in organization and organizational compensation recognition of learning transfer climate.

• ETRI Journal
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• v.46 no.2
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.

• Research in Mathematical Education
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• v.3 no.1
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• pp.57-68
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• 1999

In this paper, we investigate the comparative effectiveness of two kinds of instructional methods in transfer of mathematics learning: one based on the situated cognition, i.e. situated learning (SL) and the other based on traditional learning (TL). Both classes (of grade 2) studied addition and subtraction of 3-digit numbers. After that, they completed two written tests (Written Test 1 included computation problems, Written Test 2 included computation problems and story problems) and a real situation test. As a result, no significant differences were found between the two groups' performance on computation skill in Written Tests 1 and 2. But the SL group performed significantly better on the performance of story problem and real situation test than TL group. This result indicated that the SL made improvement in transfer of mathematics learning. As a result of interviews with 12 children of the SL group were able to use contextual resources in solving real situation as well as story problems.