• Journal of Korean Academy of Nursing Administration
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• v.14 no.3
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• pp.229-240
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• 2008

Purpose: To develop the patient classification system based on the resource utilization for reimbursement of long-term care hospitals in Korea. Method: Health Insurance Review & Assessment Service (HIRA) conducted a survey in July 2006 that included 2,899 patients from 35 long-term care hospitals. To calculate resource utilization, we measured care time of direct care staff (physicians, nursing personnel, physical and occupational therapists, social workers). The survey of patient characteristics included ADL, cognitive and behavioral status, diseases and treatments. Major category criteria was developed by modified delphi method from 9 experts. Each category was divided into 2-3 groups by ADL using tree regression. Relative resource use was expressed as a case mix index (CMI) calculated as a proportion of mean resource use. Result: This patient classification system composed of 6 major categories (ultra high medical care, high medical care, medium medical care, behavioral problem, impaired cognition and reduced physical function) and 11 subgroups by ADL score. The differences of CMI between groups were statistically significant (p<.0001). Homogeneity of groups was examined by total coefficient of variation (CV) of CMI. The range of CV was 29.68-40.77%. Conclusions: This patient classification system is feasible for reimbursement of long-term care hospitals.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.822-832
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• 2001

We have implemented an inductive learning system that learns PROSPECTOR-rule-style classification rules from sets of examples. In our a approach, a genetic algorithm is used in which a population consists of rule-sets and rule-sets generate offspring through the exchange of rules relying on genetic operators such as crossover, mutation, and inversion operators. In this paper, we describe our learning environment centering on the syntactic structure and meaning of classification rules, the structure of a population, and the implementation of genetic operators. We also present a method to evaluate the performance of rules and a heuristic approach to generate rules, which are developed to implement mutation operators more efficiently. Moreover, a method to construct a classification system using multiple learned rule-sets to enhance the performance of a classification system is also explained. The performance of our learning system is compared with other learning algorithms, such as neural networks and decision tree algorithms, using various data sets.

• Journal of Korean Society of Forest Science
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• v.34 no.1
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• pp.47-56
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• 1977

I have investigated and compared the morphology of vascular bundle shown in the section of culm wall of bamboo trees growing on Mt. Jiri which were classified by Grosser and Liese with their methods of morphological classification. The results obtained were as follows: 1. It was shown that there are no b.g.i. types of bamboo classified by Grosser and Liese among the bamboo trees on Mt. Jiri (Phyllostachys and Sasa). 2. As for the thickness of the culm wall in the culm, it was shown that the culm wall of the Phyllostachys becomes thinner in proportion to its nearness to the upper part of the tree, but no distinctive difference appeared in the Sasa. 3. The c, d, and e types of Sasa were the same as those of the Phyllostachys, but there was a vascular bundle type of the a' type, which was quite different from that of the Phyllostachys. 4. It was shown that the a', d, and e types of Sasa were distributed in a zone less than 500m above sea level, but no a' type was distributed in the high mountain area except for the c, d and e types which ranged from 600m to 1000m above sea level. Such facts mean that the vascular bundle sheath has changed in quantity because of the height of mountain. 5. In general, as compared with the Phyllostachys, the Sasa (types a, c, d and e which included a new type a) have fewer vascular bundles. 6. Considering the above results, it is thought that not by the current Sasa classification method based on observation of the the study of Sasa form the outside, but by a new method of classification based on the aspect of the physiological construction as seen from the inside wall is advanced. I believe this new method of classification to be a first step towards an epoch-making methodological advance and encourage the further study of it.

• Proceedings of the KIEE Conference
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• 1999.07d
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• pp.1624-1626
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• 1999

PD in defect of solid insulation system is very harmful since It often leads to deterioration of insulation by the combined action of the discharge ions bombarding the surface and the action of chemical compounds that are formed by the discharge. PD can indicate incipient failure, so it has been used to determine degradation of insulation. In this paper. we investigated PD in defects of solid insulation by using statical method and classified PD patterns with surface discharge, electrical tree and void discharge by using Kohonen network. we used peak charge, average discharge power, average discharge current, repetition rate, skewness, kurtosis, QN of the max pulse height vs. repetition rate $H_q(n)$ for analysis and classification.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.709-711
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• 2003

The purpose of this study is to detect the damaged red pine trees by pine wilt disease using high resolution satellite image of IKONOS Geo. IKONOS images are segmented with eCognition image processing software. A segment based maximum likelihood classification was performed to delineate the pine stand. The pine stands are regarded as a potential damage area. In order to develop a methodology to detect the location of damaged trees from the high resolution satellite image, black and white aerial photographs were used as a simulated image. The developed method based on filtering technique. A local maximum filter was adapted to detect the location of individual tree. This report presents a part of the first year results of an ongoing project.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• Environmental Engineering Research
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• v.24 no.3
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.492-494
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• 2021

The decision tree technique is used as a classification technique in machine learning. However, the decision tree has a problem of consuming a lot of speed or resources due to the problem of overfitting. To solve this problem, there are bagging and boosting techniques. Bagging creates multiple samplings and models them using them, and boosting models the sampled data and adjusts weights to reduce overfitting. In addition, recently, techniques Xgboost have been introduced to improve performance. Therefore, in this paper, we collect wifi signal data for indoor positioning, apply it to the existing method and Xgboost, and perform performance evaluation through it.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.13-21
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• 1996

The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method. I. Introduction topology employs one hidden layer with full connectivity between neighboring layers. This structure has One of the most popular neural network models been very successful for many applications. However, used for supervised learning applications has been the they have some weaknesses. For instance, the fully mutilayer feedforward network. A commonly adopted connected structure is not necessarily a good topology unless the task contains a good predictor for the full *d*dWs %BH%W* input space.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.