• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.421-422
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• 2015

Deep learning, a deepen neural network technology that demonstrates the enhanced performance of neural network analysis, has been getting the spotlight in recent years. The present study proposed a process to test the error rates of certain variables and predict big data by using R, a analysis visualization tool based on deep learning, applying the RBM(Restricted Boltzmann Machine) algorithm to deep learning. The weighted value of each dependent variable was also applied after the classification of dependent variables. The investigator tested input data with the RBM algorithm and designed a process to detect error rates with the application of R.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.276-284
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• 2022

The research aims to study the dynamics of academic procrastination and its impact on the mental health of students during the transition to distance learning during the COVID-19 pandemic. At the beginning of the COVID-19 pandemic, it was identified a declining tendency of overall rates of academic procrastination and at the same time increase in the number of carriers of mid and high levels of academic procrastination. The decline in the general rates of academic procrastination at the beginning of 2021 testifies to the adaptation processes experienced by students to the conditions of distance learning. It was documented that students' academic procrastination is accompanied by a steady negative emotional tension. During the transition to distance learning, the intensity of students' learning activity has increased, which altogether causes stress as one of the main reasons for the academic procrastination among future psychologists. The study identified a risk of academic procrastination manifestation among students for their mental health, which provides a basis for developing and testing a program to prevent the phenomenon of academic procrastination among degree-seeking students.

• Korean Management Science Review
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• v.28 no.1
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• pp.53-60
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• 2011

Time series analysis methods have been traditionally used in stock price prediction. However, most of the existing methods represent some methodological limitations in reflecting influence from external factors that affect the fluctuation of stock prices, such as oil prices, exchange rates, money interest rates, and the stock price indexes of other countries. To overcome the limitations, we propose a network based method incorporating the relations between the individual company stock prices and the external factors by using a graph-based semi-supervised learning algorithm. For verifying the significance of the proposed method, it was applied to the prediction problems of company stock prices listed in the KOSPI from January 2007 to August 2008.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2609-2614
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• 1998

In this work, we consider the forecasting performance between nearal network and Box-Jenkins method for time series data. A modified learning process is developed for neural network approach at time eries data, ie, properly adaptive learning rates selecting by orthogonal arrays and dynamic selecting of initial values using Easton's cotroller box. We can obtain good starting points with dynamic graphics approach. We use real data sets for this study : the Wolf yearly sunspot numbers between 1700 and 1988.

• Journal of Korean Dental Science
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• v.16 no.1
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• pp.47-62
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• 2023

Purpose: We aimed to investigate the objective cutoff values of unstimulated flow rates (UFR) and stimulated salivary flow rates (SFR) in patients with xerostomia and to present an optimal machine learning model with a classification and regression tree (CART) for all ages. Materials and Methods: A total of 829 patients with oral diseases were enrolled (591 females; mean age, 59.29±16.40 years; 8~95 years old), 199 patients with xerostomia and 630 patients without xerostomia. Salivary and clinical characteristics were collected and analyzed. Result: Patients with xerostomia had significantly lower levels of UFR (0.29±0.22 vs. 0.41±0.24 ml/min) and SFR (1.12±0.55 vs. 1.39±0.94 ml/min) (P<0.001), respectively, compared to those with non-xerostomia. The presence of xerostomia had a significantly negative correlation with UFR (r=-0.603, P=0.002) and SFR (r=-0.301, P=0.017). In the diagnosis of xerostomia based on the CART algorithm, the presence of stomatitis, candidiasis, halitosis, psychiatric disorder, and hyperlipidemia were significant predictors for xerostomia, and the cutoff ranges for xerostomia for UFR and SFR were 0.03~0.18 ml/min and 0.85~1.6 ml/min, respectively. Conclusion: Xerostomia was correlated with decreases in UFR and SFR, and their cutoff values varied depending on the patient's underlying oral and systemic conditions.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.108-115
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• 2020

Blended learning is a teaching method utilizing all the advantages in 'on and off-line' learning circumstances in order to enhance the learning effect and efficiency, more than the simple use of online factors in the classroom education. In this paper, we present the realization and simulation of algorithm for the realtime evaluation of low-grade and high-grade subjects in order to implement smart e-learning system, considering a lecture intelligibility. In order to grasp the levels of student's intelligibility, we simulated a function that automatically summarizes the study contents of class given by a lecturer. Especially, in administrator mode of smart e-learning system, we suggested and simulated a system in order to help the lecturer to easily manage the student's grades, and we have provided software to tell the student's intelligibility of lecture, analyzed the rate of incorrect answers, automatic judgment of lecture intelligibility and judge the weakest subject.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• Korean Journal of Microbiology
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• v.49 no.1
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• pp.51-57
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• 2013

As facilities and equipments for learning activities in college campuses are handled by mass public, their contact surfaces may function as major routes of cross-infection of microbial pathogens. However, unlike items in cafeteria which is the typical target for campus hygiene, those surfaces are not under regular surveillance or sanitary maintenance. In this study, I made a quantitative comparison of the risk of being exposed to microbial pathogens from use of learning facilities such as classrooms and library to the risk from use of cafeteria, for about 1,500 students in a college. Regarding total coliforms as surrogate model of bacterial pathogens, exposure rates were estimated for each item in learning facilities and cafeterias by devising deterministic exposure algorithms based on bacterial abundance, contract rates and transfer rates. The exposure rate in cafeterias was 1.0 CFU/day while learning facilities imposed the rate of 0.5 CFU/day, which reaches a half of the exposure rate in cafeterias. However, 70% of students were exposed more in learning facilities than cafeteria because individuals had different frequencies in using cafeteria. Based on the results, some human-contact surfaces of learning facilities, including elevator buttons, may require regular sanitary maintenance. An efficient sanitary maintenance considering seasonality in diversity of pathogens involved with cross-infections is suggested besides improvement of personal hygiene among students.

• Journal of The Korean Association For Science Education
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• v.12 no.3
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• pp.61-76
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• 1992

The purpose of this study was to develop the instructional model and teaching material to change the middle school students'conceptions of electric current into the scientific ones and to investigate the effects of the model in actual classrooms. We identified the students' ideas and their misunderstanding about the concept of eIectic current through reviewing the literatures and our in this study. Based on the above results, we developed the instructional model and designed the teaching sequence and prepare the learning materials about the unit of the electric current in middle school Our instructional model was based on 'learning cycle' developed by Lawson, but the new stage called "exploration through qualitative questions" to elicit the students' own conceptions was inserted to it. To investigate the effects or the new teaching model, the pre- and post-test using the POE type were administered to experimental group(52 students) taught with learning cycles and control group(52 students) taught with traditional styles. The results are as follows; 1) The rates of correct. predictions was varying according to the kinds of problems. And the rates of the correct. reasons of their predictions were lower than those of the predictions. 2) The mean scores of the post-test of both groups were significantly higher than those of the pre-test. We could not find statistically significant difference in theme an score between experimental group and control group after implementation of the model. But the experimental group gained higher scores than those of the control group on two problem. Therefore, although we cannot show the prominent effects of our teaching model based on learning cycles, there are some effects of our model on changing the middle school students' conceptions of electric current.