In this paper, we address the dynamic emergency medical service (EMS) systems. A dynamic location model is presented for locating and relocating a fleet of ambulances. The proposed model can control the movements and locations of ambulances in order to provide a better coverage of the demand points. The model can keep this ability under different fluctuation patterns that may happen during a given period of time. A number of numerical experiments have been carried out by using some real-world data sets. They have been collected through the French EMS system at the Hospital Henri Mondor, France. Finally, we present a comparison between the results of the introduced model and the outputs of a classical EMS dynamic location model. According to the observations, the introduced model provides a better coverage of the EMS demands.
Bipolar disorder (BD) is a severe psychiatric disorder and has two common types: type I and type II. Early diagnosis of the subtypes is very challenging particularly in adolescence. In this study, 38 adolescents are participated including 18 patients with BD I and 20 patients with BD II. The electroencephalogram signal is recorded by 19 electrodes in open eyes at resting state. After preprocessing, the state of the art methods from various domains are implemented to provide a good feature set for classifying the two groups. In order to improve the classification accuracy, four different feature selection methods named mutual information maximization (MIM), conditional mutual information maximization (CMIM), fast correlation based filter (FCBF), and double input symmetrical relevance (DISR) are applied to select the most informative features. Multilayer perceptron (MLP) neural network with a hidden layer containing five neurons is used for classification with and without applying the feature selection methods. The accuracy of 82.68, 86.33, 89.67, 84.61, and 91.83 % were observed using entire extracted features and selected features using MIM, CMIM, FCBF, and DISR methods by MLP, respectively. Therefore, the proposed method can be used in clinical setting for more validation.
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