• Journal of Korean Academy of Nursing
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• 제39권5호
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• pp.651-661
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• 2009

Purpose: This study was done using meta-analysis to examine 58 studies from studies published in the past eight years (2000 to 2007) that included variables related to adolescents' suicidal ideation. Methods: The materials for this study were based on 32 variables which were selected from masters' thesis, doctoral dissertation and articles from Journals of the Korean Academy of Nursing. Results: The classification consisted of 5 variables groups and 32 variables. In terms of effect size on risk, variables which were significant included psychological variables (0.668), socio-cultural variables (0.511), family environmental variables (0.405), school environmental variables (0.221), and personal characteristics variables (0.147). In terms of effect size on protection, variables which were significant included personal characteristics variables (-1.107), psychological variables (-0.526), family environmental variables (-0.264), and school environmental variables (-0.155). In terms of effect size on risk variables, psychological variables (0.668) were highest. In terms of effect size on protective variables, the variable of personal characteristic (-1.107) was the highest. Conclusion: While the results indicate possible risk and protective variables for suicidal ideation, but prediction is still difficult. Further study to compare adolescents with similar variables but no suicidal ideation and those with suicidal ideation is necessary.

• The Journal of Fisheries Business Administration
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• 제15권1호
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• pp.58-80
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• 1984

The classification of islands is prerequisite for establishing a development policy to vitalize many-sided function of islands. We try to classify the 440 inhabited islands which exist in Jeon-Nam area and Kyong-Nam area by means of PCA. PCA begins with making correlation matrix of orignal variables. From this matrix we can comprehend the rough relationships between two variables. Next, we look for the eigenvalues which are roots of characteristic equation of correlation matrix. The number of eigenvalues is equal to that of original variables. We choose the largest eigenvalue λ$_1$among them and then look for the eigenvector of correlation matrix corresponding to the largest eigenvalue. Linear combination of eigenvector obtained above and original variables is namely first Principal Component (PC). Using an eigenvalue criterion(λ$\geq$ 1), we choose 3 PCs in Jeon-Nam area and 2 PCs in Kyong-Nam area. But we decide to consider only two PCs in both areas to faciliate a comparative analysis. Now, loss of information is 31.7% in Jeon-Nam area and 26.64% in Kyong-Nam area. PCs extracted by preceding procedure have characteristics as follows. The first PC relates to aggregate size of islands in case of both areas. The second PC relates to income per household, factors of agricultural production and factors of fisheries production in Jeon-Nam area, but in Kyong-Nam area it means distance from island and income per household. A classification of islands can be attained by plotting component scores of each island in graph used two PCs as axes and grouping similiar islands. 6 groups are formed in Jeon-Nam area and 5 groups in Kyong-Nam area. The result of this study in kyong-Nam area accords with prior result of study.

• Journal of Preventive Medicine and Public Health
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• 제25권1호
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• pp.64-72
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• 1992

To provide the information necessary for the insurance medicine management plan, price discount rates among the insurance medicines were studied. A total of 2,107 items of insurance medicine of which prices were discounted via governmental inspections of real transactional process of insurance medicine were analysed. The conclusions are as follows; 1. Among the variables relevant to the characteristics of manufacturers, price discount rates of insurance medicines were statistically significant with production rankings of manufacturers, incorporation year, existence of investments by foreign corporation, existence of a research institute, and enrollment in the exchange. And among the variables relevant to the properties of medicines, the number of enrolled items which have the same components, classification, the date of new enrollment, the sales of items, and the number of raw materials in the items were statistically significant. 2. Stepwise multiple regression was done to identify the factors which affect the price discount rates of insurance medicines. The number of enrolled items which have the same components, production rankings of manufactures, classification number (medicines for function of tissue cells), incorporation year(1940-1949), existence of investments by foreign corporations, classification number (anti-germ medicines), number of raw materials In the items, the sales of items, and medicines whose major objective is not treatment were significant variables and the $R^2$-value for these variables was 21.2%. Considering all of the above results, for management of insurance medicines, it seems important that the real transactional prices of insurance medicines should be identified systematically, focusing on the properties which affect the price discount rates of insurance medicines.

• Journal of Korean Society of Transportation
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• 제26권3호
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• pp.53-66
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• 2008

This research is to the establishment of a conceptual framework that supports road characteristic classification from a new point of view in order to complement of the existing road functional classification and examine of traffic pattern. The road characteristic classification(RCC) is expected to use important performance criteria that produced a policy guidelines for transportation planning and operational management. For this study, the traffic data used the permanent traffic counters(PTCs) located within the national highway between 2002 and 2006. The research has described for a systematic review and assessment of how exploratory factor analysis should be applied from 12 explanatory variables. The optimal number of components and clusters are determined by interpretation of the factor analysis results. As a result, the scenario including all 12 explanatory variables is better than other scenarios. The four components is produced the optimal number of factors. This research made contributions to the understanding of the exploratory factor analysis for the road characteristic classification, further applying the objective input data for various analysis method, such as cluster analysis, regression analysis and discriminant analysis.

• Journal of Korean Clinical Nursing Research
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• 제24권1호
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• pp.113-126
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• 2018

Purpose: The purpose of this study was to revise the KPCS-1 and to standardize the three patient classification systems for general ward, ICU and NICU. The actual utilization of the KPCS-1 score and each nursing activity was evaluated and the relationships between KPCS-1 score and nursing related variables were reviewed. Methods: The 47,711 KPCS-1 scores of 6,931 patients who discharged from $1^{st}$ to $30^{th}$ April 2017 were analyzed and the statistical significance between KPCS-1 score and nursing related variables was reviewed by Generalized Estimating Equation. The revision of the KPCS-1 was carried out by Partial Least Square model. The 3 patient classification systems (KPCS-1,KPCSC and KPCSN) were standardized by professional reviews. Results: KPCS-1 was a valid instrument to express nursing condition adequately and was revised as a new version which has 34 nursing activity items. The names and terminologies of pre-existing 3 patient classification systems developed by KHNA were standardized as KPCS-GW, KPCS-ICU, KPCS-NICU. Conclusion: KPCS-1 was a valid instrument to represent diverse nursing conditions precisely and was revised as a 34-item KPCS-GW. The terminologies of the other patient classification systems by KHNA were standardized as KPCS-ICU and KPCS-NICU.

• Journal of Korean Society of Rural Planning
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• 제28권1호
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.659-672
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• 2005

This paper evaluates discretization of continuous variables to select relevant variables for supervised learning using mutual information. Three discretization methods, MDL, Histogram and 4-Intervals are considered. The process of discretization and variable subset selection is evaluated according to the classification accuracies with the 6 real data sets of UCI databases. Results show that 4-Interval discretization method based on quantiles, is robust and efficient for variable selection process. We also visually evaluate the appropriateness of the selected subset of variables.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.491-498
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• 2013

We consider support vector machines(SVM) to predict Y with p numerical variables $X_1$, ${\ldots}$, $X_p$. This paper aims to build a biplot of p explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.169-174
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• 2013

The ROC curve is drawn with two conditional cumulative distribution functions (or survival functions) of the univariate random variable. In this work, we consider joint cumulative distribution functions of k random variables, and suggest a ROC curve for multivariate random variables. With regard to the values on the line, which passes through two mean vectors of dichotomous states, a joint cumulative distribution function can be regarded as a function of the univariate variable. After this function is modified to satisfy the properties of the cumulative distribution function, a ROC curve might be derived; moreover, some illustrative examples are demonstrated.

• International journal of advanced smart convergence
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• 제5권3호
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.