• Journal of Digital Contents Society
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• v.8 no.3
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• pp.355-363
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• 2007

One of major factors for learning achievement is the student's learning preference according to his character type. In course of learning, if a student studies e-learning contents opposed to his preference, then he would be under stress and his blood pressure and heart beat be changed. For measuring unwillingness, we used spectral components in frequency domain known as stress measure. For 13 children attending kindergarten we examined S(sensing)/ N(intuition) of MBTI and presented same learning contents during 10 minutes. During learning we gathered ECG signals, changed into HRV(heart rate variability), transformed time-varying HRV signal into spectral density in frequency domain. And then, we divided it into three areas of low(LF), middle(MF), and high-frequency(HF) and calculated stress measures by rates of those frequency area. We compared estimated stress measures of S group with them of N group whether students in different group preferred different contents or not. Experimental shows that students according to MBTI type prefer different contents.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.6A
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• pp.115-127
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• 2008

The Native API is a system call which can only be accessed with the authentication of the administrator. It can be used to detect a variety of malicious codes which can only be executed with the administrator's authority. Therefore, much research is being done on detection methods using the characteristics of the Native API. Most of these researches are being done by using supervised learning methods of machine learning. However, the classification standards of Anti-Virus companies do not reflect the characteristics of the Native API. As a result the population data used in the supervised learning methods are not accurate. Therefore, more research is needed on the topic of classification standards using the Native API for detection. This paper proposes a method for re-grouping malicious codes using fuzzy clustering methods with the Native API standard. The accuracy of the proposed re-grouping method uses machine learning to compare detection rates with previous classifying methods for evaluation.

• Journal of the Korean Chemical Society
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• v.50 no.3
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• pp.247-255
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• 2006

The purpose of this study was to investigate the correlation between concepts on chemical reaction rates and concepts on chemical equilibrium in high school students. The subjects of the investigation consisted of 120 third grade students attending high school in K city of Kyunggi province. For this study, questionnaire relevant to the subject of chemical reaction rates and chemical equilibrium was developed and the answers were analyzed. As a result of the study, a large percentage of high school students answered questions on reaction rates correctly, but only a small percentage of the students could give explanations. Many high school students answered questions on the rates of forward reactions correctly, but not the questions on the rates of reverse reactions. For the concepts on chemical equilibrium, many high school students gave correct answers when faced with equilibrium questions that only required the understanding of one side of the reaction. But the students could not answer the questions requiring understanding of both forward and reverse reactions as well. Overall, there was a little high correlation between concepts on chemical reaction rates and concepts on chemical equilibrium in high school students. Especially, high school students with little understanding of reverse reaction rates did not understand that chemical equilibrium is a dynamic equilibrium. Also, high school students with little understanding of the collision mechanism regarding chemical reaction rates did not understand the effect of concentration and catalyst factors on chemical equilibrium. And the correlation between concepts on chemical reaction rates and concepts on chemical equilibrium related to concentration and catalyst factors was low. In conclusion, the formation of scientific concepts on chemical reactions rates can decrease misconceptions on chemical equilibrium. Also the teaching-learning method limited to one side of a reaction can cause difficulty in forming the concepts on chemical dynamic equilibrium. Therefore, the development of a teaching-learning method which covers both the forward and reverse reactions can be effective in helping students form the concepts on chemical equilibrium.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.468-478
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• 2018

The goal of this study is to propose a new directivity for business training based on an analysis of the learner's satisfaction, the cause of the learning effect and the cause of reenrollment in smart learning courses. The data from 878 learners of 11 companies was analyzed by ANOVA and multiple regression analysis and the following results were obtained. First of all, the satisfaction of studying by smart learning showed various results depending on the motivation, process and contents of studying. According to the results, high rates of satisfaction were observed when the people take an active part in studying, as reflected in the frequency and time of studying. Also, when the learning contents were presented in an animated manner, the satisfaction of the students was increased. Second, the motivation of the students to participate in the smart learning and study process was reflected in the frequency, time and quality of their studies, thus confirming the learning effect. Lastly, the satisfaction and effectiveness of studying by smart learning are the causes of reenrollment. Based on the analysis results, it was concluded that the corporation's support and proper compensation are needed to increase the rate of satisfaction and the effectiveness of smart learning from the corporation's perspective. Also, from the viewpoint of the smart learning system operators, it is necessary to find ways of developing the learning contents.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.75-82
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• 2018

Recently, emerging countries have been paying attention to Korean economic development policy, trying to adopt the Korean regional innovation model. Korea is also interested in exporting its regional innovation model and enhancing economic cooperation with those countries. This paper aims to analyze the capacity-building programs of the Korean regional innovation model for emerging countries and suggests policies for it. For this purpose, the local innovators' participation patterns in the process of collaborative learning/networking/interaction are investigated with a focused group-interview method. From an analysis of the programs supported by Korean organizations, this study finds that the correlation coefficient between the training time of capacity building and the participation rate of local members' collaborative learning is very high (0.975). Since the correlation coefficient between the participation rates of collaborative learning and networking is relatively low (0.667), a policy to link local collaborative learning to networking should be provided. As the correlation coefficient between the participation rates of networking and interaction is high (0.950), networking is a key to regional innovation. This study recommends activity programs to promote networking among local innovators, rather than training and consulting programs. As introduced in the Chungnam Techno Park case, this study suggests that the capacity-building program should include programs to initiate a collaborative learning network, to create a local-demand, regional innovation model, and to operate the regional innovation platform, which should be done by local innovators in the emerging countries.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.6
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• pp.44-51
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• 1999

The HMM-Net is a neural network architecture that implements the computation of output probabilities of a hidden Markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria of maximum likehood(ML) and minimization of mean squared error(MMSE) are used for learning HMM-Net classifiers. The criterion MMSE is better than ML when initial learning condition is well established. However Ml is more useful one when the condition is incomplete[3]. Therefore we propose an efficient learning method of HMM-Net classifiers using a hybrid criterion(ML/MMSE). In the method, we begin a learning with ML in order to get a stable start-point. After then, we continue the learning with MMSE to search an optimal or near-optimal solution. Experimental results for the isolated numeric digits from /0/ to /9/, a training and testing time-series pattern set, show that the performance of the proposed method is better than the others in the respects of learning and recognition rates.

• Journal of The Korean Association of Information Education
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• v.16 no.2
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• pp.245-253
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• 2012

The purpose of this study is to find out the structural relations among the changing of internet use for learning, online game use, and perceived achievement. To complete this study, we set three research models and verified our hypotheses from the research models. We used Korean Youth Panel Study (KYPS) data, which surveyed beginning with fourth grade 2,844 elementary school students. We discovered that (a) there was a statically significant individual variability in initial levels and rates of change in internet use for learning. The change of trajectory was declined. (b) We also found out both initial state and changing rate of internet use for learning positively affect perceived academic achievement. (c) Lastly our study found both the concurrent and lag effects support the developmental relation between internet use for learning and game use in young adolescents.

• Journal of The Korean Association of Information Education
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• v.13 no.1
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• pp.9-22
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• 2009

As information technology(IT) has been rapidly developed, e-learning is also growing to meet the need of lifelong education using internet. However, with the growth of e-learning has come the big problem of high dropout rates. The purpose of this present study was to identify the major factors influencing drop-out in corporate e-learning. 250 employees(persistence: n=157, dropout: n=93) who enrolled an e-learning course in S company were participated in this study. A logistic regression analysis was performed to identify predictors of dropout. It was determined that individual background(marriage, amount of study time, difficult to combine work and family), learners' characteristics and value of the course were able to predict dropout with nearly 75 percent accuracy.