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We set two objectives for this study: one is to emulate chaotic natural populations in GA (Genetic Algorithms) populations by utilizing the Logistic Chaos map model, and the other is to analyze the population fitness distribution by utilizing insect spatial distribution theory.
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Table 5 presents the associations between the angiographic characters of coronary vessels stenosis with the variants rs599839, rs64776, and rs4970834 utilizing the logistic regression analysis.
Further, the artificial neural network ARCH process (ANN-GARCH) developed by Donaldson and Kamstra (1997) augments the GJR model with multi-layer perceptron-based neural network architecture with logistic squashing functions to capture nonlinearity by utilizing the universal approximation property (Cybenko 1989) of ANN models.
A logistical model was then built by utilizing the forward selection method based on the nine genes from the previous step using the SAS PROC LOGISTIC procedure [ 8].
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First we utilize the logistic regression model to distinguish the most important symptoms of influenza infection by fitting a logistic regression model to the binary influenza infection outcome in the sample, using binary indicators of the influenza-like symptoms as predictors.
Utilizing the imputed data sets, multivariable logistic regressions were performed with the package MASS [ 50].
Data from four distinct sources were utilized in the logistic regression models (Table 1, Figure 1).
The bivariate logistic regression model, utilizing the Chi-square test, identified a significant effect of KPC on mortality (p = 0.050).
To perform logistic regression, we utilized the L1-regularized logistic regression code of Kim et al. [19].
Binary logistic regression was adopted for the quantitative analysis of the effect of socio-economic variables on the mothers' actions prior to utilizing the health facility.
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