• Genomics & Informatics
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• v.17 no.3
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• pp.30.1-30.4
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• 2019

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

• Journal of The Korean Association For Science Education
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• v.24 no.1
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• pp.121-128
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• 2004

This study was to investigate high students' conceptions of acids and bases, and their views on learning science. Multiple sources of data were collected over six months with a participation of sit tenth graders and their science teacher. The transcripts of interviews and other data were examined with an eye toward students' conceptions of acids and bases, and their views of learning science. Students' views of science are displayed the representative pattern. Each pattern is represented with an episode. Students' views of learning have been found to reflect the transmissive models of science educational practice. Students accept passive and difficult-to-modify views of the learner roles that they should play in the science classroom. Students identified science classes as conservative places, despite the introduction of science literacy as a goal of Korean science education since 1980. Behaviorism remains the major influence in their expectation, design, and practice in school science. Moreover, 'transmission' remains the persistent and dominant classroom cultural dynamic for both teaching and learning of science.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.95-106
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• 2016

Landslide is one of the natural hazards, triggered by rainfall or earthquake and it leads to damage and loss of properties and lives especially in hilly and mountainous regions. Inventory maps of the area is of much importance in order to understand the landslide phenomena in detail, conduct further studies on landslide, prepare susceptibility map and minimize risk. Inventory maps of landslides can be constructed by several methods, using multiple images through visual interpretation, using algorithms in multi-spectral or SAR images or verification from field investigation. The possible methods were explored for Sindhupalchowk district of Nepal, which was struck by massive earthquake on 2015 and landslide inventory was prepared. The inventory was analyzed for its frequency over elevation, slope aspect and dominant soil classes and also the information value for their occurrence probability.

• Korean Journal of Child Studies
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• v.30 no.5
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• pp.137-153
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• 2009

This study examined the effects on preschooler's social competence of preschooler's emotional regulation strategies and maternal attitude toward child's emotional expressiveness. Subjects were 57 3-to 5-year-old preschoolers, their mothers and 3 teachers in their classes. Data were adapted from the Social Intelligence part of Project Spectrum and analyzed by Pearson Correlation Coefficient and Stepwise Multiple Regression Analysis. Findings were that : (1) preschoolers' positive emotion regulation strategies significantly explained their social competence. (2) Older children showed higher social competence than younger children; the effect of children's age on social competence was more influential than emotional strategies or maternal attitudes. In conclusion, preschooler's emotion regulation strategies are an important factor as their social competence develops with age.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1
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• pp.104-109
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• 1997

This paper derives a new training alorithm for WHMM using the well-known MCE/GPD method with experimental results on the E-set. The derived algorithm generalizes the conventional adaptive training algorithm for WHMM, which means that HMMs of multiple competing classes can be trained at the same time. The recognition results on the E-set have shown about 15% and 12% improvement for training and test data, respectively.

• Journal of the Korean housing association
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• v.2 no.1
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• pp.1-12
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• 1991

The purpose of this study was to classify the types of housing life style. Housing life style was measured using four variables : furniture usage pattern, space usage pattern, family living pattern and heating system. A final Instrument was developed through the two stage pilot surveys. The respondents were 1,292 home-makers of the middle and high economic classes In Seoul and Daejeon, selected through stratified random sampling technique. Data were analyzed using SAS computer packages. The statistics used were frequency, percentage, Pear-3on`s correlation coefficient, Multiple Linear Regression, X2, and cluster analysis.The major findings were as follows : Five representative types of housing life style were found through cluster analysis. They were conventional minimum level life style, conventional optimum famiIy-centered life style, eclectic family-centered life style, contemporary optimum family - centered and contemporary so-cial, leasure-oriented life style.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.87-97
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• 2023

Unmanned aerial vehicles (UAVs) can capture high-resolution imagery from a variety of viewing angles and altitudes; they are generally limited to collecting images of small scenes from larger regions. To improve the utility of UAV-appropriated datasetsfor use with deep learning applications, multiple datasets created from variousregions under different conditions are needed. To demonstrate a powerful new method for integrating heterogeneous UAV datasets, this paper applies a combined segmentation network (CSN) to share UAVid and semantic drone dataset encoding blocks to learn their general features, whereas its decoding blocks are trained separately on each dataset. Experimental results show that our CSN improves the accuracy of specific classes (e.g., cars), which currently comprise a low ratio in both datasets. From this result, it is expected that the range of UAV dataset utilization will increase.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.159-168
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• 2022

No one denies the importance of online courses, which provide a very important alternative, especially for students who have jobs that prevent them from attending face-to-face in traditional classes; Engagement is one of the most important fundamental variables that indicate the course's success in achieving its objectives. Therefore, the current study aims to build a model using machine learning to predict student engagement in online courses. An online questionnaire was prepared and applied to the students of Jouf University in the Kingdom of Saudi Arabia, and data was obtained from the input variables in the questionnaire, which are: specialization, gender, academic year, skills, emotional aspects, participation, performance, and engagement in the online course as a dependent variable. Multiple regression was used to analyze the data using SPSS. Kegel was used to build the model as a machine learning technique. The results indicated that there is a positive correlation between the four variables (skills, emotional aspects, participation, and performance) and engagement in online courses. The model accuracy was very high 99.99%, This shows the model's ability to predict engagement in the light of the input variables.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.145-148
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• 2022

준지도학습 기반의 동영상 이상행동감지는 구하기 어려운 프레임 단위 레이블이 필요하지 않아 더 많은 동영상을 학습에 활용 가능한 장점이 있어 관련 연구가 활발히 진행되고 있다. 최근 제안된 기법들은 주로 UCF-Crime 이라는 실제 CCTV 동영상 데이터셋을 활용하고 있는데, 본 데이터셋은 학습 영상과 테스트 영상에서 이상행동 클래스 별 분포도가 균등하지 않다. 본 연구에서는 해당 불균형으로 인해 학습 모델이 특정 행동 클래스에 과적합될 수 있음을 보이며, 이러한 불균형을 해결하기 위해 Class-Balanced Multiple Instance Learning Loss 를 제안한다. 이를 통해 기존에 특정 클래스에 편중되었던 모델이 이상행동 종류에 좀 더 균등한 성능을 낼 수 있음을 보여준다. 특히 단순히 클래스별 정확도가 제로섬(zero sum)으로 증감하는 것이 아니라 전체적인 이상행동 판별 정확도 또한 향상됨을 실험 결과를 통해 확인할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.551-552
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• 2021

In this paper, we present a multi-class image scene classification method based on transformation learning. ImageNet classifies multiple classes of natural scene images by relying on pre-trained network models on large image datasets. In the experiment, we obtained excellent results by classifying the optimized ResNet model on Kaggle's Intel Image Classification data set.