• Progress in Medical Physics
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• v.34 no.3
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• pp.23-32
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• 2023

Purpose: In radiomics analysis, to evaluate features, and predict genetic characteristics and survival time, the pixel values of lesions depicted in computed tomography (CT) and magnetic resonance imaging (MRI) images are used. CT and MRI offer three-dimensional images, thus producing three-dimensional features (Features_3d) as output. However, in reports, the superiority between Features_3d and two-dimensional features (Features_2d) is distinct. In this study, we aimed to investigate whether a difference exists in the prediction accuracy of radiomics analysis of lung cancer using Features_2d and Features_3d. Methods: A total of 38 cases of large cell carcinoma (LCC) and 40 cases of squamous cell carcinoma (SCC) were selected for this study. Two- and three-dimensional lesion segmentations were performed. A total of 774 features were obtained. Using least absolute shrinkage and selection operator regression, seven Features_2d and six Features_3d were obtained. Results: Linear discriminant analysis revealed that the sensitivities of Features_2d and Features_3d to LCC were 86.8% and 89.5%, respectively. The coefficients of determination through multiple regression analysis and the areas under the receiver operating characteristic curve (AUC) were 0.68 and 0.70 and 0.93 and 0.94, respectively. The P-value of the estimated AUC was 0.87. Conclusions: No difference was found in the prediction accuracy for LCC and SCC between Features_2d and Features_3d.

• Journal of the Korean Society of Costume
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• v.14
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• pp.185-198
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• 1990

The purpose of this study is to identify and profile Korean women's fashion opinion leaders on demographic, psychological and communication channels dimensions. The questionnaire was administered to 1204 students from a purposively selected. women's universities in Seoul. The data was analyzed using $X^2$-test, t-test, multiple regression analysis and discriminant analysis, The significance level was set at. 05. The major findings derived from analysis are as follows: 1. Fashion opinion leaders are generally come from families with higher income, more education and higher occupational status than followers. 2. Fashion opinion leaders are more likely to be exhibitionistic, self-confident, individualistic, risk taking and gregarious than followers. 3. Fashion opinion leaders are more exposed to impersonal communication media, especially to fashion magazines than followers. These findings imply an obvious usefulness for both manufacturers in the apparel industry as well as retailers to help them in the identification of their target market for the introduction and acceptance of fashion items.

• Journal of the Korean Applied Science and Technology
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• v.37 no.4
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• pp.1052-1065
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• 2020

This study analyzed the effects of professional baseball PPL advertising speed on brand awareness, brand attitude, and behavioral intention for professional baseball consumers. The purpose of this study is to present a method that can be used as a variety of marketing utilization strategies of professional baseball teams and parent companies. This study was conducted on 411 professional baseball consumers. For data processing, frequency analysis, reliability analysis, reliability analysis, and multiple regression analysis were performed using SPSS 25.0 Window Version. Then, the average variance extraction index (AVE) and construct validity (CR) were calculated to verify convergent validity and discriminant validity. In addition, confirmatory factor analysis (CFA) was performed using AMOS 25.0. As a result, first, it was found that entertainment, informativeness, and discomfort, which are sub-factors of PPL advertisement speed, have a significant effect on brand recognition. Second, entertainment, information, and discomfort, which are sub-factors of PPL advertising speed, have a significant effect on brand attitude. Third, entertainment, informativeness, and discomfort, which are sub-factors of PPL advertisement speed, have a significant effect on behavioral intention. Fourth, it was found that brand awareness and brand attitude have a significant effect on behavioral intention.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.133-144
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• 2012

This study focused on estimating influential factors of traffic accidents and analyzing traffic accident severity of elderly and non elderly using traffic accident data. In order to reclassify elderly and non elderly traffic accident by a statistical method from entire traffic accident data, multiple discriminant analysis was applied. Also ordered logit model was applied for analyzing traffic accident severities using traffic accident severities as an independent variable and transportation facilities, road conditions and human characteristics as dependent variables. As results of the comparison between elderly and non elderly traffic accident, the traffic accident severity was affected by the age, types of traffic accidents, human characteristics and road conditions as well. Also, transportation facilities and road conditions affected to more elderly traffic accident than non elderly. Therefore, traffic accident severity would be decreased with the improvement of transportation facilities and road conditions for the elderly.

• Journal of the korean society of oceanography
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• v.31 no.4
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• pp.217-231
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• 1996

The distribution patterns of the benthic macrofauna of Han Estuary and Kyonggi Bay and the controlling environmental factors were studied at twenty-five stations in spring and fall of 1989. As a result, four biological groups were established as follows : Crassostrea gigas-Balanus reticulatus (Group I), Heteromastus filiformis-Mediomastus californiensis-Lumbrineris spp. -Sternaspis scutata-Tharyx sp. 1-Diopatra bilobata (Group II-A), Haustoriids-Phoxocephalids-Moerella rutila (Group II-B) and Nephtys chemulpoensis (Group II-C in March) and indistinctive group which was composed of common species (II-C in September). Results of the habitat analysis revealed that most of the dominant species showed narrow ranges of habitat niche in March and relatively wide ranges in September. Based on multiple discriminant analysis, the critical environmental factors governing their distributions are salinity in the regions of Yomha and Sokmo Channel in Han Estuary and sediment types in Kyonggi Bay. Also, sediment instability during the rainy season due to run-off was assumed to play a major role in the species composition of the benthic communities and their distribution in the study area.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.55-74
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• 2005

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper employs a relatively new machine learning technique, support vector machines (SVMs). to bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use grid search technique using 5-fold cross-validation to find out the optimal values of the parameters of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM. we compare its performance with multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.475-486
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• 2015

Mining the microarray data accumulated in the public data repositories can save experimental cost and time and provide valuable biomedical information. Big data analysis pooling multiple data sets increases statistical power, improves the reliability of the results, and reduces the specific bias of the individual study. However, integrating several data sets from different studies is needed to deal with many problems. In this study, I limited the focus to the cross platform classification that the platform of a testing sample is different from the platform of a training set, and suggested a simple classification method based on rank. This method is compared with the diagonal linear discriminant analysis, k nearest neighbor method and support vector machine using the cross platform real example data sets of two cancers.

• The Research Journal of the Costume Culture
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• v.8 no.1
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• pp.1-14
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• 2000

The purposed of this was (1) to investigate the beliefs, attitudes and buying intention of well-known brand apparel among female college students, and (2) to identify the factors that might distinguish those who have high intention to purchase well-known brand apparel and those who have low intention in terms of individual characteristics and social influence. The data were collected trough questionnaire from random samples of 291 female college students. Statical analysis of factor analysis, χ²-test, t-test and multiple discriminant were performed in analyzing the data. 1. 63% of 291 respondents investigated were identified as having high intention to buy well-known brand apparel. 2. Those who have high intention to buy well-known brand apparel have significantly higher beliefs on well-known brand apparel. 3. Those who have high intention to buy well-known brand apparel have significantly more favorable attitude toward buying well-known brand apparel. 4. Two groups of high intention and low intention to buy well-known brand apparel have significantly different profiles in terms of social influences, values, personality and demographic variables.

• Korean Journal of Child Studies
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• v.27 no.6
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• pp.35-51
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• 2006

The purpose of the present study was to identify variables for predicting types of outsiders and possible mitigating factors in bullying situations. Participants were 344 $5^{th}$, $6^{th}$ and $7^{th}$ grade students(159 boys and 185 girls). Involvement of outsiders in bullying was examined by proportion. Major findings were that; (1) Over half of the sample(65.4%) were involved in bullying by either actively reinforcing(bully-followers; 6.4%) or passively observing(bystanders; 59.0%); 34.6% were defenders of victims. (2) Multiple discriminant analysis yielded a function of 3 variables(empathy, risk burden, and prosocial moral reasoning) that was effective in classifying bully-followers, bystanders, and victim-defenders. Empathy and prosocial moral reasoning predicted victim-defenders and risk burden predicted bully-followers and bystanders.

• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.273-290
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• 2006

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.