• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.96-101
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• 2021

This paper presents a machine learning model that predicts stroke risks in atrial fibrillation patients using public big data. As the training data, 68 independent variables including demographic, medical history, health examination were collected from the Korean National Health Insurance Service. To predict stroke incidence in patients with atrial fibrillation, we applied deep neural network. We firstly verify the performance of conventional statistical models (CHADS2, CHA2DS2-VASc). Then we compared proposed model with the statistical models for various hyperparameters. Accuracy and area under the receiver operating characteristic (AUROC) were mainly used as indicators for performance evaluation. As a result, the model using batch normalization showed the highest performance, which recorded better performance than the statistical model.

• Journal of the Korean Geographical Society
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• v.38 no.2
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• pp.275-291
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• 2003

It was essential to develop the DB-type teaching and teaming material for geography instruction using a method of ICT. The DB-type teaching and learning material was considered as a alternative in solving the problems of web-based geography instruction. Accordingly, in this study, the geography image DB program as developed, and based on this program the CD-ROM called GEO-DB, having the function of electronic dictionary of geography image for geography teaching and teaming was made. The GEO-DB was composed of 3,060 geography images collected by teachers and learners. The GEO-DB was made to be used simply by teachers and learners. Especially, the portfolio function was Included in the GEO-DB, and that was focused to the instructional system design of teacher and the self-directed teaming ability development of learner. Teachers and learners using this GEO-DB assessed that because the GEO-DB had the easiness of use, the speed of reference and the unlimitedness of extension, it could enlarge the possibility of using a method of In, and it could contribute to the development of geography teaming ability and the change of geography teaming attitude.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.551-560
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• 2019

In the advanced information age, learning is an activity in which learners access information resources through computers and Internet to acquire and evaluate information on their own. The emergence of an online learning platform based on the fourth industrial revolution technology is developing into an environment in which elementary and secondary learners learn and study based on constructivism learning theory. In the online learning environment, the researches on the information retrieval ability of the elementary and secondary learners and the self-directed learning ability were found to be highly related. However, it is necessary to analyze the relation between information retrieval ability and self-directed learning ability through a cross-sectional study that is limited to specific curriculum and contents and expands the longitudinal research. In this study, the panel data of the Seoul Education Longitudinal Study collected over 8 years are used to find the difference in self-directed learning ability according to the longitudinal pattern of information retrieval ability and frequency.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.56-62
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• 2019

Recently, research using artificial neural networks has further expanded the field of neural network optimization and automatic structuring from improving inference accuracy. The performance of the machine learning algorithm depends on how the hyperparameters are configured. Open-source hyperparameter optimization software can be an important step forward in improving the performance of machine learning algorithms. In this paper, we review open-source hyperparameter optimization softwares.

• 대한공업교육학회지
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• v.36 no.1
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• pp.167-190
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• 2011

The purpose of this study is to identify the effect of problem solving with task-based activities on understanding of major concepts and learning attitude in 'Applications of ICT' subject. In teaching the 4th class of 'Applications of ICT' subject, problem solving with reasoning task-based activities are used for the experimental groups and instructor-oriented teaching for the comparative groups. The results are as follows: First, no meaningful difference was found in the pretest result of concepts of ICT, while posttest found that the students with problem solving with reasoning task-based activities in experimental group marked average 5.87 point higher than the control group and showed meaningful difference at significance level p<.05. Dividing concepts about Information Communication Technology into four domains, there were no meaningful difference between two groups in the concept test about communication principles and methods and network, while the test results about the other two concepts, that is, expressions and patterns of information and compositions and types of communication network, showed the meaningful difference at significance level p<.05. Second, the research proved that the experimental group with problem solving with reasoning task-based activity teaching, compared to the control group with lecture, showed desirable change in learning attitude. From the results, the solving with reasoning task-based activity model is better teaching-learning method compared to lecture, revealing positive change in understanding major concepts of information and communication technology and learning attitude.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.57-65
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• 2023

Emergency numbers, such as 112 and 119, are used in many countries to connect people in need with emergency services such as police, fire, and medical assistance. We describe development directions of intelligent response technology for emergency calls. The development of this technology refers to enhancing the efficiency and effectiveness of response systems by using advanced methods such as artificial intelligence, machine learning, and big data analytics. We focus on a system that assists the receptionist of an emergency call. In the future, the recognition rate and decision-making accuracy of intelligent response technologies should be improved considering characteristics of public safety and emergency domain data. Although the current technology remains at the level of assisting a receptionist, a fully autonomous response technology is expected to emerge in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Korean Journal of Comparative Education
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• v.28 no.2
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• pp.123-151
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• 2018

The purposes of this study were to examine the current status of ICT in all ASEAN countries and to provide implications for Korea to find appropriate ways to support and collaborate with HEIs in ASEAN countries. To achieve these purposes, ASEAN countries were categorized into 3 groups based on the development stages of ICT, and the key ICT initiatives, current facts about ICT, and related issues were analyzed. The results of the study were as follows: Group 1 countries, Brunei, Malaysia, and Singapore, with relatively well-established ICT infrastructure, have established their own ICT policies and initiated e-learning programs. Group 2 countries, Indonesia, Philippines, Thailand, and Vietnam, which have relatively well-developed ICT infrastructure with existing regional gaps, showed different uses of ICT in higher education. Philippines and Thailand established their own policies based on national ICT master plans while Indonesia focused on MOOCs and Vietnam initiated cyber university projects. Group 3 countries, Cambodia, Lao PDR, and Myanmar, with the least developed ICT infrastructure in ASEAN, have also tried to develop national level strategies to utilize ICT in higher education. However, insufficient and inadequate ICT infrastructure created issues and challenges for these countries to successfully initiate ICT policies. This study suggested that it is necessary to take into serious consideration the national differences when collaborating with and supporting ASEAN countries due to the variation of ICT development stages and different levels of using ICT in higher education among ASEAN countries.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.139-145
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• 2020

Due to the rapid development of computer technology, online learning using ICT technology is already quickly settling in our lives. Music education was conducted mainly in an offline-based environment, but research is being conducted to switch to online because there is no time and space constraint of online education and interactive education led by learners is possible. In this study, we propose design and implement an adaptive learning system to enable adaptive learning online among music education. This system has the following advantages. First, by providing an LMS-based platform, one can solve the social education problem corresponding to economic and geographical factors. Second, both objective learning feedback provided automatically by the online adaptive harmony learning system and teaching feedback. Third, learners can be provided with recommended answers to given harmony exercises. The adaptive online learning system of harmony will lead professors and learners to effectively teach and study.