• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.911-920
• /
• 2004

We try receiver operating characteristic(ROC) curves by neural networks of logistic function. The models are shown to arise from model classification for normal (diseased) and abnormal (nondiseased) groups in medical research. A few goodness-of-fit test statistics using normality curves are discussed and the performances using neural networks of logistic function are conducted.

• Development and Reproduction
• /
• v.15 no.1
• /
• pp.9-15
• /
• 2011

The population growth and reproduction of Tetrahymena pyriformis were studied under shaken (aerobic) and unshaken (anaerobic) conditions by applying the growth models, exponential and logistic growth models and the population growth of Tetrahymena was showed the logistic growth model under both, shaken and unshaken conditions and also, the more oxygenated samples had greater population size (N) and three times faster growth rate (r) than less oxygenated samples during incubation periods.

• Communications of the Korean Mathematical Society
• /
• v.38 no.4
• /
• pp.1163-1173
• /
• 2023

In this paper, we prove the Hyers-Ulam stability and Mittag-Leffler-Hyers-Ulam stability of a differential equation of Logistic growth in a population by applying Laplace transforms method.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.569-572
• /
• 2008

The possibility to extent the solution in human detection problem for plug-in on vision-based Human Computer Interaction domain is very attractive, since the successful of the machine leaning theory and computer vision marriage. Bayesian logistic regression is a powerful classifier performing sparseness and high accuracy. The difficulties of finding people in an image will be conquered by implementing this Bavesian model as classifier. The comparison with other massive classifier e.g. SVM and RVM will introduce acceptance of this method for human detection problem. Our experimental results show the good performance of Bavesian logistic regression in human detection problem, both in trade-off curves (ROC, DET) and real-implementation compare to SVM and RVM.

• Communications for Statistical Applications and Methods
• /
• v.17 no.1
• /
• pp.117-126
• /
• 2010

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.04a
• /
• pp.601-604
• /
• 2000

In this paper, we compare the classification performance of both data fusion and clustering algorithms (Data Bagging, Variable Selection Bagging, Parameter Combining, Clustering) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are (1) correlation among input variables (2) variance of observation (3) training data size and (4) input-output function. Since the relationship between input & output is not typically known, we use Taguchi design to improve the practicality of our study results by letting it as a noise factor. Experimental study results indicate the following: Clustering based logistic regression turns out to provide the highest classification accuracy when input variables are weakly correlated and the variance of data is high. When there is high correlation among input variables, variable bagging performs better than logistic regression. When there is strong correlation among input variables and high variance between observations, bagging appears to be marginally better than logistic regression but was not significant.

• Journal of Korea Port Economic Association
• /
• v.24 no.1
• /
• pp.85-102
• /
• 2008

The purpose of this study is to analyze the effects of Logistic Information System on performance by efficiency of internal operation and organizational innovation. The results of this study as follows : The characteristic variable evaluation model that extended from the performance(user's value, perceived usefulness) of Logistic Information System were verified meaningfully. In this study, the efficiency of internal operation and organizational innovation were very important factor, to analyze the effects of Logistic Information System on performance. This study expect that Logistic Information System will achieve their Logistic Information System competitiveness through continuous quality measurement and improvement to increase the performance.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.22
• /
• pp.9731-9737
• /
• 2014

Large scale secular registry or surveillance systems have been accumulating vast data that allow mathematical modeling of cancer incidence and mortality rates. Most contemporary models in this regard use time series and APC (age-period-cohort) methods and focus primarily on predicting or analyzing cancer epidemiology with little attention being paid to implications for designing cancer registry, surveillance or evaluation initiatives. This research models age-specific cancer incidence rates using logistic growth equations and explores their performance under different scenarios of data completeness in the hope of deriving clues for reshaping relevant data collection. The study used China Cancer Registry Report 2012 as the data source. It employed 3-parameter logistic growth equations and modeled the age-specific incidence rates of all and the top 10 cancers presented in the registry report. The study performed 3 types of modeling, namely full age-span by fitting, multiple 5-year-segment fitting and single-segment fitting. Measurement of model performance adopted adjusted goodness of fit that combines sum of squred residuals and relative errors. Both model simulation and performance evalation utilized self-developed algorithms programed using C# languade and MS Visual Studio 2008. For models built upon full age-span data, predicted age-specific cancer incidence rates fitted very well with observed values for most (except cervical and breast) cancers with estimated goodness of fit (Rs) being over 0.96. When a given cancer is concerned, the R valuae of the logistic growth model derived using observed data from urban residents was greater than or at least equal to that of the same model built on data from rural people. For models based on multiple-5-year-segment data, the Rs remained fairly high (over 0.89) until 3-fourths of the data segments were excluded. For models using a fixed length single-segment of observed data, the older the age covered by the corresponding data segment, the higher the resulting Rs. Logistic growth models describe age-specific incidence rates perfectly for most cancers and may be used to inform data collection for purposes of monitoring and analyzing cancer epidemic. Helped by appropriate logistic growth equations, the work vomume of contemporary data collection, e.g., cancer registry and surveilance systems, may be reduced substantially.

• Korean Management Science Review
• /
• v.30 no.2
• /
• pp.117-132
• /
• 2013

The objective of this paper is to provide an improved technology appraisal model, which considers a variety of macroeconomic variables such as consumer price index and producer price index. The improved model was built using cross correlation analysis and logistic regression analysis. The AUROC analysis showed that goodness-of-fit of the proposed model turned out to be improved than that of the existing model. The model proposed in the paper would be helpful for making a reasonable investments and financing decision, lessening the default rates by systematic risk management, and enhancing the technology commercialization capabilities.

• Proceedings of the Korea Society for Simulation Conference
• /
• 1998.10a
• /
• pp.46-50
• /
• 1998

본 연구는 최근에 국내기업의 물류비용절감을 위한 노력의 일환으로 추진되고 있는 적정 물류장비의 선정을 위한 시뮬레이션 모델의 개발이다. 본 연구에서는 제조공장에서 소비자들에게 공급되는 2단계 물류시스템에서 물류재고와 운송수단을 고려하여 최소 물류비용의 운송대안선정을 위한 의사결정을 할 수 있도록 개발하였다. 이를 위하여 2가지의 시뮬레이션모델의 전산프로그램을 개발하고 응용사례를 통하여 출력을 예시하였다.