• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.109-117
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• 2020

An edutainment system aims to help learners to recognize problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Edutainment contents can be usefully applied to education and training in the both scientific and industrial areas. Our present work proposes an edutainment system that can be applied to a drug discovery process including virtual screening by using intuitive multi-modal interfaces. In this system, a stereoscopic monitor is used to make three-dimensional (3D) macro-molecular images, with supporting multi-modal interfaces to manipulate 3D models of molecular structures effectively. In this paper, our system can easily solve a docking simulation function, which is one of important virtual drug screening methods, by applying gaming factors. The level-up concept is implemented to realize a bio-game approach, in which the gaming factor depends on number of objects and users. The quality of the proposed system is evaluated with performance comparison in terms of a finishing time of a drug docking process to screen new inhibitors against target proteins of human immunodeficiency virus (HIV) in an e-drug discovery process.

• The Journal of the Korea Contents Association
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• v.9 no.3
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• pp.361-371
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• 2009

LAMS(learning activity management system)[1] is one of the useful tools for designing and managing effectively the learning activities such as web search, chat, forum, grouping, and board. Even if LAMS has been upgraded to support the methods for making e-Learning contents conveniently, it does not have a method to communicate with external educational contents (EEC) made by external tools like Flash, Java, Visual C++, and so on. LAMS, which has been operated on Web environment, should manage all EECs like video and dynamic educational contents as educational contents in LAMS database. However, the current LAMS does not support the functionalities which can provide information of EECs to LAMS database and can also access any information about EECs from the database yet. In this paper, we propose the communication mechanism between the LAMS and EECs for solving the problem. In special, the mechanism makes many statistical data by using the information, and provides them for reflecting in education, and can control various learning management that was impossible under the original LAMS. Based on the proposed mechanism, teachers using LAMS can make more various educational contents and can manage them in the system.

• Journal of Korean Home Economics Education Association
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• v.23 no.3
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• pp.69-90
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• 2011

To develop the descriptive evaluation questionnaire with high validity and reliability on the Clothing and Textiles section of the middle school Technology & Hone Economics textbook, apply it to students and analyze its results. We made out a draft for descriptive evaluation questionnaire that was based upon the concrete establishment of the goal and the range of evaluation. We also made a rubric for scoring as well as sample answer-sheets. Finally, we completed a total of twenty three descriptive evaluation questions and we applied it to sixty five 2nd-grade students in two classes in a middle school. Descriptive evaluation questionnaire exhibited the relative high validity on each question. Moreover, three graders gave the same score on each question of descriptive evaluation, suggesting that descriptive evaluation questionnaire has the high inter-grader reliability and the strong correlation. But, low academic achievement was generally observed in the subjects. They had difficulty in describing their knowledge via their own language and drawing up accurate and detailed answers. They recognized the positive aspects of descriptive evaluation questionnaire, but they felt it uncomfortable due to study-burden and description itself. To overcome these limitations, it is required that students should experience various materials related to subject contents in classes as well as textbooks, concentrate themselves on finding solutions for problems, expand their scope, and practice describe them in advance. Therefore, the additional training for description evaluation questionnaire will be necessary for the more efficient and discriminative questionnaire. Also the questionnaire with high validity and reliability should be developed and the aggressive and voluntary participation of teachers will be needed.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.734-751
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• 2021

The purpose of this study is to prepare basic data to reflect polar literacy education in the school curriculum. The perception about the polar regions, teaching experience, and polar-related cognitive and affective characteristics of teachers were investigated. The survey was conducted among 56 elementary, middle, and high school teachers from schools from 10 major cities and surrounding regions, based on their perceptions of the polar region, current teaching status, polar knowledge, and beliefs and attitudes toward polar region and climate change. Results showed that although teachers' polar information efficacy was low, they positively evaluated the status of educators in resolving polar and climate change problems, and prioritized global citizenship values over practical purposes. The experience of teaching polar region and climate change issues at schools varied across subjects and non-subjects, but showed a passive aspect in teaching development, such as wanting to be provided with consolidated learning materials. On the cognitive aspect, teachers revealed an ambiguous understanding of the mechanisms and processes by which polar change and climate influence each other. On the affective aspect, most teachers showed strong beliefs and attitudes for polar-related issues beyond the school level, but their behavior choices were relatively lower. Based on the results, we propose the following as recommendations: providing opportunities and materials to promote polar knowledge, discovering educational materials in various contexts to form values and attitudes, developing educational materials from polar research materials, identifying misconceptions about polar knowledge among students and teachers, strengthening elementary school teachers' polar literacy, and cultivating positive attitudes and values toward polar issues.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.99-110
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• 2023

This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.293-301
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• 2023

Unstandardized medical data collection and management are still being conducted manually, and studies are being conducted to classify CT data using deep learning to solve this problem. However, most studies are developing models based only on the axial plane, which is a basic CT slice. Because CT images depict only human structures unlike general images, reconstructing CT scans alone can provide richer physical features. This study seeks to find ways to achieve higher performance through various methods of converting CT scan to 2D as well as axial planes. The training used 1042 CT scans from five body parts and collected 179 test sets and 448 with external datasets for model evaluation. To develop a deep learning model, we used InceptionResNetV2 pre-trained with ImageNet as a backbone and re-trained the entire layer of the model. As a result of the experiment, the reconstruction data model achieved 99.33% in body part classification, 1.12% higher than the axial model, and the axial model was higher only in brain and neck in contrast classification. In conclusion, it was possible to achieve more accurate performance when learning with data that shows better anatomical features than when trained with axial slice alone.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.523-543
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• 2009

We conduct a survey to find out the academic achievement factors for 484 second grade students at two middle schools in Busan city and Kyungsangnam Do, respectively. The survey questionnaire includes home environment and background, students' personal character, relationships with friends, learning attitudes towards improving problem solving, variables related to teaching methods and teachers, the school's computer facilities, mobile class by students levels, private education current situations, etc. Private education current situations are presented according to regions. Based on survey data we perform factor analysis to find major factors affecting mathematics academic achievement. We analyze the characteristics of the major factors. We also propose basic data and implications to mathematics educators and mathematics teachers at middle schools for improving middle school mathematics education quality.

• Journal of Korean Elementary Science Education
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• v.37 no.4
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• pp.339-351
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• 2018

The purpose of this study is to examine the effect of cooperative making problems and solving problems for formative assessment at finish stage on science academic achievement and scientific attitude. This study is conducted in 51 sixth-graders of two classes. The experimental group was provided with a teaching-learning course based on cooperative making problem and solving problem at finish stage. And the control group was provided with general classes based on the contents in teacher's guidebooks. The experiment was performed with the second and third units of the sixth grade, for about two month and obtained the following results: First, students prefer to make supply-type items than multiple choices. And by the Bloom's revised taxonomy of educational objectives, students prefer to make the problem types of 'Factual Knowledge' and 'Conceptual Knowledge'. Also students prefer to make the problem types of 'Understanding' and 'Applying'. Second, cooperative problem making and solving problems at finish stage has same effect on academic achievement in comparison to teacher-driven activity. Third, the experimental group made statistically significant difference in self-efficiency, contrary to the general science classes. Especially, it turned out that a meaningful effect was discovered to a cooperativity, openness. Finally, it turned out that many students thought cooperative making problem and solving problem at finish stage gave the help approving their cooperativity and openness at the investigation of awareness.