Fuzzy logic is shown to be a very promising mathematical approach for modelling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation planning problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. This paper presents an analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes.
hormones on QT interval in males versus females, we constructed ''male'' and ''female'' cell models using Faber-Rudy model of the guinea pig myocyte. The female model incorporated physiological concentrations of 17b-estradiol and progesterone measured in the follicular and luteal phases of the menstrual cycle, and predicts changes in APD at different stages of the menstrual cycle that are consistent with clinically observed QT interval fluctuations. The male model was developed to reflect changes induced by physiology concentrations of Testosterone. The models suggest protective effects of testosterone and progesterone to prevent APD prolongation and reduce QT interval, while estrogen significantly increase QT and susceptibility to drug-induced arrhythmias.
Feature selection is aimed to select a reducing number of subset features with high predictive information and remove irrelevant features with minimal predictive information. In this paper, we propose an ensemble approach for selecting features, using multiple feature selection techniques and combining the same to yield more robust and stable results. Multiple feature ranking techniques assemblage is performed in two steps. The first step necessitates creating a set of different feature selectors while the second step combines the results of all feature ranking techniques. The application of this method has been tested using accident dataset to increase predictive performance of accident in Kolkata. After the feature selection methods, this paper also explains significance of data mining classification algorithms to build classification models on the accident datasets with various selected subset of features. Further, the classification models are assessed in terms of the AUC performance metric.
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