• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.191-199
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• 2013

The main objective of this paper is to investigate why university students use their smart phones in class and to develop a predictive model of smart phone use behavior that consisted of perceived benefit, perceived cost, attitude, social effect, intention, and habit. The proposed model is tested using survey data collected from 120 university student smart phone users. PLS analysis show as following: At first, intention and habit are significant predictors of smart phone use behavior in class. Secondly, perceived benefit and perceived cost as well as attitude and social effect are the factors affecting smart phone usage in class.

• Journal of the Korean Society of Earth Science Education
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• v.8 no.2
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• pp.152-163
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• 2015

In this study, to raise the interest and curiosity of students and at the same time to learn science concepts meaningfully for students, the teaching and learning program was developed by applying the Frayer model. The purpose of this study was to find out the Effect of Elementary Science Teaching Program with Frayer model on Learning Motive and Learning Achievement. To this end, the 6th grade classroom of A-elementary school located in Seogwipo-city was selected the experimental group (26 patients). And the other 6th grade classroom in the same school was selected to the comparative group (27 patients). The experimental group was conducted applying the Frayer model. Comparison group has been conducted lesson program in accordance with the general science class teacher guide. Was through a pre-test of science learning motivation and academic achievement level can be assumed in the same group. After completing the experimental treatment by conducting a post-mortem examination was statistically validated. In this study, the following conclusions were obtained. First, elementary science class which applied Frayer model had the effect of to improve the scientific motivation. In particular, attention (p <.01), association (p <.01), confidence (p <.01) in the experimental group were higher than the scores of the comparative group, the difference was significant. Second, the Frayer model applied to elementary science class had a significant effect on improving science achievement. The experimental group which applied Frayer model was higher than the comparative group in science achievement post-test comparison. Between the groups showed a significant difference between the two groups (p <.01). The above findings, Elementary science class which applied Frayer model can be concluded to be effective in science and science achievement motivation. Therefore, applying the Frayer model of elementary science class could be useful in science teaching and learning methods. In addition, when it is determined through the previous study, applying the Frayer model classes will be able to derive a meaningful learning also subjected to a number of fields and areas.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.107-115
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• 2023

High turbidity in source water can have adverse effects on water treatment plant operations and aquatic ecosystems, necessitating turbidity management. Consequently, research aimed at predicting river turbidity continues. This study developed a multi-class classification model for prediction of turbidity using LightGBM (Light Gradient Boosting Machine), a representative ensemble machine learning algorithm. The model utilized data that was classified into four classes ranging from 1 to 4 based on turbidity, from low to high. The number of input data points used for analysis varied among classes, with 945, 763, 95, and 25 data points for classes 1 to 4, respectively. The developed model exhibited precisions of 0.85, 0.71, 0.26, and 0.30, as well as recalls of 0.82, 0.76, 0.19, and 0.60 for classes 1 to 4, respectively. The model tended to perform less effectively in the minority classes due to the limited data available for these classes. To address data imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied, resulting in improved model performance. For classes 1 to 4, the Precision and Recall of the improved model were 0.88, 0.71, 0.26, 0.25 and 0.79, 0.76, 0.38, 0.60, respectively. This demonstrated that alleviating data imbalance led to a significant enhancement in Recall of the model. Furthermore, to analyze the impact of differences in input data composition addressing the input data imbalance, input data was constructed with various ratios for each class, and the model performances were compared. The results indicate that an appropriate composition ratio for model input data improves the performance of the machine learning model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.256-263
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• 2014

This research aims is to confirm the influence of a teacher's teaching style and teaching career on instructional analysis. Through this, the differences in the relevant elements' view points on a class and those in teachers' analysis view points on class criticism and instructional analysis at the education field can be investigated. For this study, the teaching styles of 198 elementary school teachers were categorized, their teaching careers were checked as teaching career and set them as covariate, and the differences in the view points on the analysis of the class climates were verified depending on each teaching style. As the research result, meaningful differences were found in four areas of the elements of class climates analysis, that is, creativity, vitality, precision, and gentleness. In the analysis of the class climates, the teachers with a professional style among the teaching styles gave the highest grades to creativity in the same class, those with a facilitating style to vitality, those with a role model style to precision, and those with a facilitating and role model style to gentleness. On the other hand, those with an authoritative and a delegating style were proved to give the lowest grades in general class climates. It means that teachers with different teaching styles have different viewpoints when analyzing a class, and those with a professional, a role model and a facilitating style have a relatively stronger intention to analyze a class through reflective introspection and permissive recognition.

• Structural Engineering and Mechanics
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• v.18 no.4
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• pp.461-474
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• 2004

A proposed incremental model for the solution of a general class of convex programming problems is introduced. The model is an extension of that developed by Mahmoud et al. (1993) which is limited to linear constraints having nonzero free coefficients. In the present model, this limitation is relaxed, and allowed to be zero. The model is extended to accommodate those constraints of zero free coefficients. The proposed model is applied to solve the elasto-static contact problems as a class of variation inequality problems of convex nature. A set of different physical nature verification examples is solved and discussed in this paper.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.4
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• pp.310-322
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• 2022

In this report, we apply an anomaly detection algorithm to a mobile oral health care application. In particular, we have investigated one class YOLOv3 as an anomaly detection model to classify pictures of mouths which will be used as inputs in the following machine learning model. We have achieved outstanding performances by proposing appropriate annotation strategies for our data sets and modifying the loss function. Moreover, the model can classify not only oral and non-oral pictures but also output preprocessed pictures that only contain the area around the lips by using the predicted bounding box. Thus, the model performs prediction and preprocessing simultaneously.

• 대한공업교육학회지
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• v.45 no.1
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• pp.20-41
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• 2020

The purpose of this study is to explore ways to improve teaching and learning in specialized high schools by investigating the relationship between creativity and class satisfaction in classes using the NFTM-TRIZ model for specialized high school students. In order to achieve the purpose of the research, first, the differences between the applied effects, experiments, and control groups were analyzed when the NFTM-TRIZ model was applied. Second, when the NFTM-TRIZ model was applied, it was analyzed whether there was a significant difference in creativity and class satisfaction by group size. The conclusions of this study are as follows. First, as a result of comparing the preand post-tests of the experimental group and the control group applying the NFTM-TRIZ model through the t-test, the experimental group showed significant differences in creative spontaneity, identity, attachment, curiosity and class satisfaction. Second, in experimental groups with the NFTM-TRIZ model, the size of groups of 4 and 6, rather than the size of groups of 2, had positive effects on class satisfaction. Therefore, the NFTM-TRIZ model based on collaborative learning was effective as a teaching and learning method that promoted creativity and satisfaction of students in specialized high schools.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.99-111
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• 1997

This paper presents an application of an artificial neural net to the implementation of decision class analysis (DCA), together with the generation of a decision model influence diagram. The diagram is well-known as a good tool for knowledge representation of complex decision problems. Generating influence diagram model is known to in practice require much time and effort, and the resulting model can be generally applicable to only a specific decision problem. In order to reduce the burden of modeling decision problems, the concept of DCA is introduced. DCA treats a set of decision problems having some degree of similarityz as a single unit. We propose a method utilizing a feedforward neural net with supervised learning rule to develop DCA based on influence diagram, which method consists of two phases: Phase l is to search for relevant chance and value nodes of an individual influence diagram from given decision and specific situations and Phase II elicits arcs among the nodes in the diagram. We also examine the results of neural net simulation with an example of a class of decision problems.

• Smart Media Journal
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• v.13 no.10
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• pp.28-34
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• 2024

Alzheimer's disease is a form of dementia that can be managed by identifying the disease in its initial phases. In recent times, numerous computer-aided diagnostic techniques utilizing magnetic resonance imaging (MRI) have demonstrated promising outcomes in the categorization of Alzheimer's disease (AD). The OASIS MRI dataset was utilized which has 80,000 brain MRI images. It is suggested to resample this dataset as it is highly imbalanced and posed a challenge in preventing bias toward majority class while employing the convolution neural network (CNN) model for classification. This paper examines and extracts patterns and features of 461 patients taken from the OASIS dataset. The research has aimed at utilizing the Base Model of EfficientNetV2B0 with custom classification layers, and simplified custom CNN model, also exploring Multi-class classification across four distinct classes: Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented in addition to binary classification as Non-Demented and treating other classes as demented. Furthermore, different dataset sizes were experimented with 5,000 and 20,000 for each class to be discussed in this paper. The experiment results indicate that EfficientNetV2B0 achieved the accuracy of 98% in binary classification, 99% in multiclass. Whereas custom sequential CNN model in multiclass classification presents the accuracy of 96% for 20,000 dataset size and 98% for 80,000 dataset size.

• Journal of Korea Multimedia Society
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• v.9 no.6
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• pp.693-699
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• 2006

Object-oriented databases (OODBs) have been adopted for managing non-standard applications such as computer-aided design (CAD), office document management and many multimedia applications. One of the major characteristics of OODBs is class hierarchy where a subclass is allowed to inherit the definitions defined on its superclasses. In this paper, I present the evaluation metrics for class hierarchy quality in OODBs. These metrics are developed to determine if a concurrency control scheme can achieve good performance or not on a given class hierarchy. I first discuss the existing concurrency control schemes for OODBs. Then I provide evaluation metrics based on structural information and access frequency information in class hierarchies. In order to discuss significance of the proposed performance metrics, an analytical model is developed. Analysis results show that the performance metrics are important factor in concurrency control performance. I consider both single inheritance and multiple inheritance. The proposed metrics can be used to provide guidelines on how to design class hierarchy of an OODB for maximizing the performance of concurrency control technique.