• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.153-172
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• 2022

In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific Papers (U: 154.38, CM: 0.79), Number of Citation (U: 95.86, CM: 0.70), Conference (U: 68.67, CM: 0.57), Grade (U: 10.13, CM: 0.29), Grant (U: 35.40, CM: 0.36), IPR (U: 19.81, CM: 0.27), Qualification (U: 2.57, CM: 0.26), and Grant Awardee (U: 2.66, CM: 0.26). To analyze the factors, two data mining classifiers were involved, Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). Evaluation of the data mining classifier with an accuracy score for BPNN of 95 percent, and SVM of 92 percent. The essence of this analysis is not to find the highest accuracy score, but rather whether the factors can pass the test phase with the expected results. The findings of this study reveal the factors that have a significant impact on research performance and vice versa.

• 무역상무연구
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• 제35권
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• pp.147-178
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• 2007

This study clarified a study of relationship of strategic alliance structure factor, partner selection criteria and performance awareness on the container liners alliance. In order to obtain such objective of study existing literature variables suitable to the container liner were perused and extracted. Research models for research development and three study hypothesis were set out and scope of investigation and samples were chosen. The research hypothesis are followings. H1: The factors of strategic alliance motivation influence the performance awareness. H2: The strategic alliance structure factors influence the performance awareness. H3: The factors of partner selection criteria influence the performance awareness. In the result of the empirical study, the hypothesis 1, hypothesis 2 were supported completely and hypothesis 3 was partially supported.

• 유통과학연구
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• 제16권5호
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• pp.17-29
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• 2018

Purpose - This study analyzed the success strategy of Korean small & medium cosmetics exporting companies to enter the Southeast Asian market. Research design, data, and methodology - The independent factors are classified into firm capacity, financial factor, institutional factor, and operational factor. The results of the selection of distributor partners of cosmetics related export companies as a were classified as financial performance and non - financial performance. In order to analyze this, 65 Korean small and medium export companies were recruited through structured online questionnaire for 44 days from September 18, 2017 to October 31, 2017. These data were analyzed by frequency analysis, correlation analysis, factor analysis and regression analysis using SPSS. Results - The Cronbach's alpha coefficient was found to be 0.846. Factor analysis between variables revealed that the eigen value exceeded 1 and was considered valid. As a result of the correlation analysis between the variables, the financial factor and the corporate's competence showed the highest correlation with 0.774. Conclusions - Among the factors influencing the financial performance of the exporting firms, the factors influencing the financial performance of the exporting companies are the factors that influence the non - financial performance rather than the financial performance.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• 한국전자거래학회지
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• 제17권1호
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• pp.53-74
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• 2012

본 연구에서는 비즈니스 프로세스 관리(Business Process Management, BPM) 환경에서 자원의 성능에 영향을 미치게 되는 여러 요소를 고려하여 인적자원을 선택하는 방법론을 개발한다. 스케줄링에 있어서 자원의 선택 문제는 작업 수행도에 직접적인 영향을 미치기 때문에 중요한 문제로 인식되어져 왔다. 비록 많은 문제에 있어서 전통적인 자원선택 방법론이 의미를 가져왔으나, 인적자원을 다루는데 있어서는 가장 좋은 방법론이라고 볼 수 없다. 인적자원은 작업부하, 작업소요시간, 작업간 시간 등의 다양한 요소에 의해서 영향을 받는 특이한 요소이며 본 연구는 이러한 다양한 요소를 고려하여 작업자를 선택하는 방법론을 제시한다. 이를 위해서 베이지안 네트워크를 사용하며, 앞서 기술한 여러 요소들을 한꺼번에 고려하기 위한 베이지안 선택규칙(Bayesian Selection Rule, BSR)을 도입하였다. 또한, 시뮬레이션을 통해서 본 연구에서 개발된 방법론이 대기시간, 작업수행시간과 사이클 타임을 줄일 수 있음을 보였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2010년도 춘계 학술논문 발표대회 1부
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• pp.201-205
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• 2010

Earthwork is an essential initial work discipline in construction projects and open to significant impacts of several factors such as weather, site conditions, soil conditions, underground installations and available construction machinery, calling for careful planning by managers. However, selection and combination of construction machinery and equipment for earthwork still depends on experience or intuition of managers in construction sites, with much room left for proper management in terms of cost, schedule and environmental load control. This research aims to analyze the performance of earthwork equipment and establish relations among various factors affecting a model for optimizing selection and combination of earthwork equipment as a precursor to the development of such model. We expect the conclusions herein to contribute to optimizing selection and combination of earthwork equipment and provide basic inputs for the development of applicable model that can save costs, reduce schedule and mitigate environmental load.

• 산업공학
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• 제22권1호
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• pp.44-55
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• 2009

We propose a method for selection of input-output factors in DEA. It is designed to select better combinations of input-output factors that are well suited for evaluating substantial performance of DMUs. Several selected DEA models with different input-output factors combinations are evaluated, and the relationship between the computed efficiency scores and a single performance criterion of DMUs is investigated using decision tree. Based on the results of decision tree analysis, a relatively better DEA model can be chosen, which is expected to well represent the true performance of DMUs. We illustrate the effectiveness of the proposed method by applying it to the efficiency evaluation of 101 listed companies in steel and metal industry.

• 한국식생활문화학회지
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• 제19권1호
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• pp.1-11
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• 2004

The specific purposes of this study are that ; 1) to investigate the distinguished selection attributes on performance of Japanese chain restaurant according to general characteristics of the respondent ; 2) to find out relationships between selection attributes on performance for Japanese restaurant and customer's satisfaction Frequency analysis. one-way ANOVA, reliability analysis, factor analysis, multiple regression were used to analyze the data. Total 350 questionnaires were distributed and 312 were replied(89.14%). Selection attributes on performance for Japanese chain restaurant was divided into 7 factors. There are Factor1 'Store Image & Kindness', Factor2 'Sanitation & Taste', Factor3 'Approximation & Children's Menu', Factor4 'Delivery & Business Hours', Factor5 'Food Quantity & Korean Food', Factor6 'Service & Parking' Factor7 'Price & Publicity'. Monthly income, eating-out expense per once and type of companion have significant influences on selection attributes for performance. Customer's total satisfaction is significantly affected by selection attributes on performance. Factor7 'Price & Publicity' has the most significant influence on customer's satisfaction. We expect that the results can be used to provide basic information to plan marketing strategies, and take improved customer's satisfaction for Japanese chain restaurants.

• Journal of Yeungnam Medical Science
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• 제34권1호
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• pp.62-68
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• 2017

Background: This study was conducted to examine the academic achievements of first year medical students in one medical school based on their characteristics and student selection factors of admission. Methods: The admission scores of student selection factors (Medical Education Eligibility Test [MEET], grade point average [GPA], English test score and interview) and demographic information were obtained from 61 students who had interviewed (multiple mini interview [MMI]) for admission (38 graduate medical school students in 2014, 23 medical college-transfer students in 2015). T-tests and ANOVA were used to examine the differences in academic achievement according to the student characteristics. Correlations between admission criteria scores and academic achievements were examined. Results: MEET score was higher among graduate medical students than medical college transfer students among student selection factors for admission. There were no significant differences in academic achievement of first grade medical school between age, gender, region of high school, years after graduation and school system. The lowest interview score group showed significantly lower achievement in problem-based learning (PBL) (p=0.034). Undergraduate GPA score was positively correlated with first grade total score (r=0.446, p=0.001) among admission scores of student selection factors. Conclusion: Students with higher GPA scores tend to do better academically in their first year of medical school. In case of interview, academic achievement did not lead to differences except for PBL.

• 대한안전경영과학회지
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• 제11권2호
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• pp.153-158
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• 2009

An optimal selection problem of SCM system is one of the critical issues for the company's competitiveness and performance under global economy. This paper presents a hierarchy model consisted of characteristic factors for introducing SCM system and an AHP (Analytic Hierarchy Process) based decision-making model for SCM system evaluation and selection. The proposed model can systematically construct the objectives of SCM system selection to meet the business goals. This paper focuses on selecting an optimal SCM system considering both all decision factors and sub-decision factors of a hierarchy model. Especially, the benefit/cost analysis is applied to choose SCM system. A case study shows the feasibility of the proposed model and the model can help a company to make better decision-making in the SCM system selection problem.