• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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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.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.35
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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.

• Journal of Distribution Science
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• v.16 no.5
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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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• v.25 no.1
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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.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.53-74
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• 2012

This study developed a method for selection of available human resources for incomingjob allocation that considers factors affecting resource performance in the business process management (BPM) environment. For many years, resource selection has been treated as a very important issue in scheduling due to its direct influence on the speed and quality of task accomplishment. Even though traditional resource selection can work well in many situations, it might not be the best choice when dealing with human resources. Humanresource performance is easily affected by several factors such as workload, queue, working hours, inter-arrival time, and others. The resource-selection rule developed in the present study considers factors that affect human resource performance. We used a Bayesian Network (BN) to incorporate those factors into a single model, which we have called the Bayesian Selection Rule (BSR). Our simulation results show that the BSR can reduce waiting time, completion time and cycle time.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2010.05a
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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.

• IE interfaces
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• v.22 no.1
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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.

• Journal of the Korean Society of Food Culture
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• v.19 no.1
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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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• v.34 no.1
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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.

• Journal of the Korea Safety Management & Science
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• v.11 no.2
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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.