• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.685-695
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• 2017

The traditional mixture of experts (ME) modeled the gate network using a certain parametric function. However, if the assumed parametric function does not properly reflect the true nature, the prediction strength of ME would become weak. For example, the parametric ME often uses logistic or multinomial logistic models for the network model. However, this could be very misleading if the true nature of the data is quite different from those models. Although, in this case, we may develop more flexible parametric models by extending the model at hand, we will never be free from such misspecification problems. In order to alleviate such weakness of the parametric ME, we propose to use the semi-parametric mixture of experts (SME) in which the gate network is estimated in a non-parametrical way. Based on this, we compared the performance of the SME with those of ME and neural networks via several simulation experiments and real data examples.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.203-212
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• 2002

This study examined the predictive power of data mining algorithms by comparing the performance of logistic regression and decision tree algorithm, called CHAID (Chi-squared Automatic Interaction Detection), On the contrary to the previous studies, decision tree performed better than logistic regression. We have also developed a CDSS (Clinical Decision Support System) with three modules (doctor, nurse, and patient) based on data warehouse architecture. Data warehouse collects and integrates relevant information from various databases from hospital information system (HIS ). This system can help improve decision making capability of doctors and improve accessibility of educational material for patients.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.40
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• pp.65-96
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• 2008

This paper analyses the results of a survey questionnaire which is made for logistics service providers in Shanghai, China. Based on 177 valid providers' responses, the study results show that the logistics industry of China consists mainly of small and medium-sized companies. Furthermore, most of the logistics companies are highly capable of providing traditional logistics services and lack of the capability to provide other value-added logistics services. Their self-assessments indicate that they generally perform well in different types of performance measures. This study indicates that the market for 3PL services in China has a reasonable potential for further development, though 3PL practices are still at a nascent stage in China. This paper presents full details and implications of the results of the survey and then tries to provide some helpful suggestion for the development of Chinese logistics companies.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4170-4180
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• 2022

The task complexity (TACOM) measure was previously developed to quantify the complexity of proceduralized tasks conducted by nuclear power plant operators. Following the development of the TACOM measure, its appropriateness has been validated by investigating the relationship between TACOM scores and three kinds of human performance data, namely response times, human error probabilities, and subjective workload scores. However, the information reflected in quantified TACOM scores is still insufficient to determine the levels of complexity of proceduralized tasks for human reliability analysis (HRA) applications. In this regard, the objective of this study is to suggest criteria for determining the levels of task complexity based on logistic regression between human error occurrences in digitalized main control rooms and TACOM scores. Analysis results confirmed that the likelihood of human error occurrence according to the TACOM score is secured. This result strongly implies that the TACOM measure can be used to identify the levels of task complexity, which could be applicable to various research domains including HRA.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.445-455
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• 2022

In this paper, we introduce existing Bayesian methods for high-dimensional sparse regression models and compare their performance in various simulation scenarios. Especially, we focus on the variational Bayes approach proposed by Ray and Szabó (2021), which enables scalable and accurate Bayesian inference. Based on simulated data sets from sparse high-dimensional linear regression models, we compare the variational Bayes approach with other Bayesian and frequentist methods. To check the practical performance of the variational Bayes in logistic regression models, a real data analysis is conducted using leukemia data set.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2822-2843
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• 2023

All aspects of human life have become increasingly dependent on data in the last few decades. The development of several applications causes an enormous issue on data volume in current years. This information must be safeguarded and kept in safe locations. Massive volumes of data have been safely stored with cloud computing. This technology is developing rapidly because of its immense potentials. As a result, protecting data and the procedures to be handled from attackers has become a top priority in order to maintain its integrity, confidentiality, protection, and privacy. Therefore, it is important to implement the appropriate security measures in order to prevent security breaches and vulnerabilities. An improved version of Modular Encryption Standard (IMES) based on layered modelling of safety mechanisms is the major focus of this paper's research work. Key generation in IMES is done using a logistic map, which estimates the values of the input data. The performance analysis demonstrates that proposed work performs better than commonly used algorithms against cloud security in terms of higher performance and additional qualitative security features. The results prove that the proposed IMES has 0.015s of processing time, where existing models have 0.017s to 0.022s of processing time for a file size of 256KB.

• Structural Engineering and Mechanics
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• v.91 no.3
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• pp.279-289
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• 2024

The purpose of this study is to represent a useful alternative for the preliminary seismic vulnerability assessment of existing reinforced concrete buildings by introducing a statistical approach employing the binary logistic regression technique. Two different predictive statistical models, namely full and reduced models, were generated utilizing building characteristics obtained from the damage database compiled after 1999 Düzce earthquake. Among the inspected building parameters, number of stories, overhang ratio, priority index, soft story index, normalized redundancy ratio and normalized lateral stiffness index were specifically selected as the predictor variables for vulnerability classification. As a result, normalized redundancy ratio and soft story index were identified as the most significant predictors affecting seismic vulnerability in terms of life safety performance level. In conclusion, it is revealed that both models are capable of classifying the set of buildings being severely damaged or collapsed with a balanced accuracy of 73%, hence, both are able to filter out high-priority buildings for life safety performance assessment. Thus, in this study, having the same high accuracy as the full model, the reduced model using fewer predictors is proposed as a simple and viable classifier for determining life safety levels of reinforced concrete buildings in the preliminary seismic risk assessment.

• Journal of Preventive Medicine and Public Health
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• v.46 no.3
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• pp.139-146
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• 2013

Objectives: Cigarette smoking prevalence among adolescent males in rural areas of Korea has increased in recent years. The aim of this study was to explore the factors related to increasing trends in cigarette smoking among adolescent males living in rural areas. Methods: The raw data from the Korea Youth Risk Behavior Web-based Survey from 2006 to 2009 were used. Data were analyzed by using the method of complex survey data analysis considering complex sampling design. Multiple logistic regression models were used to explore the factors affecting cigarette smoking. We evaluated the linear time trends in the prevalence of factors that were related to current smoking status and the linear time trends in cigarette smoking in groups stratified by the exposure to each factor using logistic regression models. Finally, we examined the contributions of the factors to the time trends in cigarette smoking by adjusting for each of those factors in the baseline regression models and changes in the adjusted odds ratio by survey year. Results: A statistically significant increasing trend in smoking was observed after adjusting for the factors affecting cigarette smoking. Significant factors related to cigarette use were perceived stress, experience with depression, current alcohol drinking, exposure to secondhand smoke, and academic performance. The factor related to increasing trends in cigarette smoking was academic performance. Conclusions: Stress about academic performance is an important factor affecting the increase in cigarette smoking among adolescent males in a rural area of Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.