• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.321-340
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• 2017

The purpose of this study is to design a teaching model applying a problem-based learning model and to analyze the educational benefits that students felt. This study initiated a problem-based learning model from an analysis of existing studies. Through the consultation of experts, the scenario was modified. The problem was designed according to the design stage activity (problem analysis, PBL class suitability judgment, contents analysis, learner analysis, environment analysis, PBL operating environment decision, PBL class) and Strategic Design (problem situation design, learning resource design, Facilitation design, operational strategy design, evaluation design, PBL operating environment design). Based on the initial scenarios, the researcher analyzed the results of the problem - based learning through learners' reflective diaries. The researcher was able to confirm that the critical thinking and creativity were improved in the first PBL problem situation, and the method for smooth communication and cooperation was utilized. The results on analyzing the effects of education about the first problem-based learning and students' opinions about modification will be used for the second revision and supplement of the course design. This study introduces a case of PBL course development and expects further application and research.

• Korean Journal of Radiology
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• v.24 no.2
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• pp.155-165
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• 2023

Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. Materials and Methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.212-218
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• 2020

There are dozens of different types of lesions that can be diagnosed through chest X-ray images, including Atelectasis, Cardiomegaly, Mass, Pneumothorax, and Effusion. Computed tomography(CT) test is generally necessary to determine the exact diagnosis and location and size of thoracic lesions, however computed tomography has disadvantages such as expensive cost and a lot of radiation exposure. Therefore, in this paper, we propose a deep learning algorithm for judging the presence or absence of lesions in chest X-ray images as the primary screening tool for the diagnosis of thoracic lesions. The proposed algorithm was designed by comparing various configuration methods to optimize the judgment of presence of lesions from chest X-ray. As a result, the evaluation rate of lesion presence of the proposed algorithm is about 1% better than the existing algorithm.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Journal of the Earthquake Engineering Society of Korea
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• v.26 no.2
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• pp.71-82
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• 2022

This paper presents the effect on the inelastic behavior and structural performance of concrete and filled steel pipe through a numerical method for reliable judgment under various load conditions of the CJS composite structural system. Variable values optimized for the CJS synthetic structural system and the effects of multiple variables used for finite element analysis to present analytical modeling were compared and analyzed with experimental results. The Winfrith concrete model was used as a concrete material model that describes the confinement effect well, and the concrete structure was modeled with solid elements. Through geometric analysis of shell and solid elements, rectangular steel pipe columns and steel elements were modeled as shell elements. In addition, the slip behavior of the joint between the concrete column and the rectangular steel pipe was described using the Surface-to-Surface function. After finite element analysis modeling, simulation was performed for cyclic loading after assuming that the lower part of the foundation was a pin in the same way as in the experiment. The analysis model was verified by comparing the calculated analysis results with the experimental results, focusing on initial stiffness, maximum strength, and energy dissipation capability.

• Journal of the Korean Society of Earth Science Education
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• v.6 no.2
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• pp.152-158
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• 2013

The purpose of this work is to investigate pre-service elementary teachers' belief in science gifted education. To do that, from September to November 2012, this research had been conducted with 42 students who were in the third year of P University of Education. The conclusions of this work are presented as follows: First, the pre-service elementary teachers considered exploration ability to be the most important talent for the gifted students in science, and chose task commitment as the next most important. They regarded intelligent ability and leadership ability as the relatively less important. Secondly, regarding the most important tool in choosing scientifically gifted students, the pre-service elementary teachers preferred creativity test. It was found that they considered the intelligence test and academic achievements, which require intelligent ability, to be the less important. Thirdly, regarding the special knowledge related to science gifted education, the pre-service elementary teachers considered pedagogical knowledge about the gifted to be the most important. Fourthly, regarding a class type for gifted students in science, the pre-service elementary teachers most preferred project learning. Project learning is a learning method in which students choose an interesting problem and solve the problem in cooperation with group members. It is the most widely used exploration class in gifted education. It is in the same context as the result that exploration ability is the most important factor to elementary gifted students in science. This work revealed that, with regard to a talent for the gifted in science, judgment of the gifted in science and science gifted education, the potential ability and affective ability of gifted students are considered to be more important than their intelligent ability. Therefore, it was analyzed that pre-service elementary teachers' belief in the gifted students in science is almost consistent with the recent trend of gifted education.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• Journal of IKEEE
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• v.24 no.2
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• pp.419-425
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• 2020

The market size of immersive 360-degree video contents, which are noted as one of the main technology of the fourth industry, increases every year. However, since most of the images are distributed through illegal distribution networks such as Torrent after the DRM gets lifted, the damage caused by illegal copying is also increasing. Although filtering technology is used as a technology to respond to these issues in 2D videos, most of those filtering technology has issues in that it has to overcome the technical limitation such as huge feature-point data volume and the related processing capacity due to ultra high resolution such as 4K UHD or higher in order to apply the existing technology to immersive 360° videos. To solve these problems, this paper proposes a feature-point recognition ratio improvement method for immersive 360-degree videos using deep learning technology.

• The Journal of Bigdata
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• v.5 no.2
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• pp.215-229
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• 2020

The interest in machine learning is growing in all industries, but it is difficult to apply it to real-world tasks because of inexplicability. This paper introduces a case of developing a financial customer churn prediction model for a securities company, and introduces the research results on an attempt to develop a machine learning model that can be explained using the SHAP Value methodology and derivation of interpretability. In this study, a total of six customer churn models are compared and analyzed, and the cause of customer churn is inferred through the classification and data analysis of SHAP Value and the type of customer asset change. Based on the results of this study, it would be possible to use it as a basis for comprehensive judgment, such as using the Value of the deviation prediction result that can infer the cause of the marketing manager's actual customer marketing in the future and establishing a target marketing strategy for each customer.