• 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.

• 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.

• Korea Journal of Hospital Management
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• v.18 no.4
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• pp.54-77
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• 2013

This paper is a comparison study about leadership training transfer factors between physicians working in large scale hospitals and managers working in firms. To fulfill this purpose, this study conducted a regression analysis on 101 managers and 59 physicians who had attended similar leadership training programs more than 16 hours recently in order to identify the differences on the learning transfer factors. 6 factors such as Learner readiness, Performance self-efficacy, (so far as Trainee Characteristics group), Organization Culture, Supervisor's tangible incentives and Supervisor's intangible support, (so far as Work environment group), Content Validity & Transfer Design (so far Training Design group) were used as independent variables while the personal Managerial Capability Increase and Leadership Capability Increase were used as dependent variables. And also we used 5 factors as control variables ; Job style (Manager or Physician), Age, Gender, Working years and Organization size. Here are the summary of major findings ; first, there were statistically significant differences between the learning transfer factors in leadership training programs for managers and those of physicians. Second, there were also statistically significant differences among trainees' working years and their organization size factors while age and gender do not affect the learning transfer factors. Third, for the physician's leadership training the practitioners should focus on two factors ; Organization Culture and Learner readiness.

• Journal of Biomedical Engineering Research
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• v.39 no.5
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• pp.213-219
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• 2018

Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3333-3350
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• 2019

The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1293-1308
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• 2020

In fine-tuning-based transfer learning, the size of the dataset may affect learning accuracy. When a dataset scale is small, fine-tuning-based transfer-learning methods use high computing costs, similar to a large-scale dataset. We propose a mid-level feature extractor that retrains only the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with the performance of low- and high-level feature extractors, as well as the fine-tuning method. First, the mid-level feature extractor takes a shorter time to converge than other methods do. Second, it shows good accuracy in validation loss evaluation. Third, it obtains an area under the ROC curve (AUC) of 0.87 in an untrained test dataset that is very different from the training dataset. Fourth, it extracts more clear feature maps about shape and part of the chest in the X-ray than fine-tuning method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.655-657
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• 2021

It is challenging to find breast ultrasound image training dataset to develop an accurate machine learning model due to various regulations, personal information issues, and expensiveness of acquiring the images. However, studies targeting transfer learning for ultrasound breast cancer images classification have not been able to achieve high performance compared to radiologists. Here, we propose an improved transfer learning model for ultrasound breast cancer classification using publicly available dataset. We argue that with a proper combination of ImageNet pre-trained model and optimizer, a better performing model for ultrasound breast cancer image classification can be achieved. The proposed model provided a preliminary test accuracy of 99.5%. With more experiments involving various hyperparameters, the model is expected to achieve higher performance when subjected to new instances.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.413-420
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• 2023

For a long time, researchers have presented excellent results in the field of image retrieval due to many studies on CBIR. However, there is still a semantic gap between these search results for images and human perception. It is still a difficult problem to classify images with a level of human perception using a small number of images. Therefore, this paper proposes an image classification model using deep learning-based transfer learning to minimize the semantic gap between images of people and search systems in image retrieval. As a result of the experiment, the loss rate of the learning model was 0.2451% and the accuracy was 0.8922%. The implementation of the proposed image classification method was able to achieve the desired goal. And in deep learning, it was confirmed that the CNN's transfer learning model method was effective in creating an image database by adding new data.