• Journal of Korean Academy of Nursing Administration
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• v.11 no.2
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• pp.209-218
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• 2005

Purpose: This study was to setup the basis on hospital and national nursing manpower estimation accurately according to apply patient classification system of Song's study to China hospital system. Method: This study was surveyed to 964 patients at surgical and medical ward on Yanbian University Hospital in China from 17th to 31th January, 2005. Results: There was study results to test hypotheses for estimating optimal nursing manpower as follows. First, a trimodel classification scheme was developed which suggested three categories of patients as minimal care(category 1), moderate care(category 2), intensive care(category 3). Second, there was not significant difference with nursing time by sex. Third, there was not significant difference with nursing time by medical wards. Fourth, there was not significant difference with average nursing care time for each category of patients. Category 1 was estimated to spend average 19.59minutes for patients, Category 2 was about 35.68 minutes, Category 3 was 72.07minutes respectively. Total nursing hours was 62,610 minutes. Conclusion: Patient classification system of Song's study is validity for optimal nursing manpower of hospital in China.

• Journal of the Korean Society for information Management
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• v.30 no.3
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• pp.49-70
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• 2013

The purposes of this study are to analyze and compare current state of subject directories of the health field in eight internet bookstores in domestic and abroad. A comparative analysis was carried out for KDC and DDC, using names of subdivisions within the health field of internet bookstore. Also, user interviews to find their information needs about the health field in internet bookstore were conducted. And then, based upon the findings, this study proposed a design principle and a new classification for the health field of internet bookstores. With evaluations of the experts from the field, a final classification schedule (1 class, 11 division, 60 subdivision, and 16 section) was suggested. The results of this study can be used for a foundation of classifying health resources efficiently in internet bookstores and other web sites.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.185-192
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• 2013

In this paper, proposed to improve the performance of speech and mixed content signal classification using MFCC based on GMM probability model used for the MPEG USAC(Unified Speech and Audio Coding) standard. For effective pattern recognition, the Gaussian mixture model (GMM) probability model is used. For the optimal GMM parameter extraction, we use the expectation maximization (EM) algorithm. The proposed classification algorithm is divided into two significant parts. The first one extracts the optimal parameters for the GMM. The second distinguishes between speech and mixed content signals using MFCC feature parameters. The performance of the proposed classification algorithm shows better results compared to the conventionally implemented USAC scheme.

• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.275-288
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• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.555-556
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• 2021

In this paper, we propose a malware family classification scheme using malware visualization and transfer learning. The malware can be easily reused or modified. However, traditional malware detection techniques are vulnerable to detecting variants of malware. Malware belonging to the same class are converted into images that are similar to each other. Therefore, the proposed method can classify malware with a deep learning model that has been verified in the field of image classification. As a result of an experiment using the VGG-16 model on the Malimg dataset, the classification accuracy was over 98%.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.103-111
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• 2007

The spectral information based image analysis, visual interpretation and automatic classification have been widely carried out so far for remote sensing data processing. Yet recently, many researchers have tried to extract the spatial information which cannot be expressed directly in the image itself. Using the texture and wavelet scheme, we made a wavelet-based texture fusion image which includes the advantages of each scheme. Moreover, using these schemes, we carried out image classification for the urban spatial analysis and the geological structure analysis around the caldera area. These two case studies showed that image classification accuracy of texture image and wavelet-based texture fusion image is better than that of using only raw image. In case of the urban area using high resolution image, as both texture and wavelet based texture fusion image are added to the original image, the classification accuracy is the highest. Because detailed spatial information is applied to the urban area where detail pixel variation is very significant. In case of the geological structure analysis using middle and low resolution image, the images added by only texture image showed the highest classification accuracy. It is interpreted to be necessary to simplify the information such as elevation variation, thermal distribution, on the occasion of analyzing the relatively larger geological structure like a caldera. Therefore, in the image analysis using spatial information, each spatial information analysis method should be carefully selected by considering the characteristics of the satellite images and the purpose of study.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.139-148
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• 2010

This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.

• Journal of IKEEE
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• v.22 no.1
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• pp.14-20
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• 2018

The purpose of training a convolutional neural network (CNN) is to obtain weight factors that give high classification accuracies. The initial values of hyper-parameters affect the training results, and it is important to train a CNN with a suitable hyper-parameter set of a learning rate, a batch size, the initialization of weight factors, and an optimizer. We investigate the effects of a single hyper-parameter while others are fixed in order to obtain a hyper-parameter set that gives higher classification accuracies and requires shorter training time using a proposed VGG-like CNN for training since the VGG is widely used. The CNN is trained for four datasets of CIFAR10, CIFAR100, GTSRB and DSDL-DB. The effects of the normalization and the data transformation for datasets are also investigated, and a training scheme using merged datasets is proposed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.81-88
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• 2017

In this paper, we classified the weaknesses of C/C++ programs listed in CWE based on the diagnostic information produced at each stage of program compilation. Our classification identifies which stages should be responsible for analyzing the weaknesses. We also present algorithmic frameworks for detecting typical weaknesses belonging to the classes to demonstrate validness of our scheme. For the weaknesses that cannot be analyzed by using the diagnostic information, we separated them as a group that are often detectable by the analyses that simulate program execution, for instance, symbolic execution and abstract interpretation. We expect that classification of weaknesses, and diagnostic information accordingly, would contribute to systematic development of static analyzers that minimizes false positives and negatives.