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    A new algorithm based on artificial bee colony algorithm for energy demand forecasting in Turkey
    (IEEE, 2015) Uguz, Harun; Hakli, Huseyin; Baykan, Omer K.
    In this study, an energy demand forecasting algorithm based on the Artificial Bee Colony with Variable Search Strategies (ABCVSS) method was proposed in order to determine Turkey's long-term energy demand. Linear and quadratic equations were used for energy demand forecasting and the coefficients of the equations were determined by means of the ABCVSS method. With the ABCVSS method, an attempt was made to enhance the local and global searching capacity of the ABC algorithm by using five different search strategies. GDP, population, imports and exports data of the period from 1979 to 2005 were chosen as the input parameters for the proposed method. Long-term energy demand was predicted through one scenario and the obtained performance from the proposed method was compared to those obtained from PSO, ACO and HAP algorithms in the literature. It was determined that the proposed method is statistically more successful than the other methods.
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    Predicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methods
    (ELSEVIER, 2014) Celik, Guner; Baykan, Omer K.; Kara, Yakup; Tireli, Hulya
    Background: The aim of the present study was to evaluate the performance of 2 different multivariate statistical methods and artificial neural networks (ANNs) in predicting the mortality of hemorrhagic and ischemic patients within the first 10 days after stroke. Methods: The multilayer perceptron (MLP) ANN model and multivariate statistical methods (multivariate discriminant analysis [MDA] and logistic regression analysis [LRA]) have been used to predict acute stroke mortality. The data of total 570 patients (230 hemorrhagic and 340 ischemic stroke), who were admitted to the hospital within the first 24 hours after stroke onset, have been used to develop prediction models. The factors affecting the prognosis were used as inputs for prediction models. Survival or death status of the patients was taken as output of the models. Results: For the MLP method, the accuracies were 99.9% in a training data set and 80.9% in a testing data set for the hemorrhagic group, whereas 97.8% and 75.9% for the ischemic group, respectively. For the MDA method, the training and testing performances were 89.8%, 87.8% and 80.6%, 79.7% for hemorrhagic and ischemic groups, respectively. For the LRA method, the training and testing performances for the hemorrhagic group were 89.7% and 86.1%, and for the ischemic group were 81.7% and 80.9%, respectively. Conclusions: Training and test performances yielded different results for ischemic and hemorrhagic groups. MLP method was most successful for the training phase, whereas LRA and MDA methods were successful for the test phase. In the hemorrhagic group, higher prediction performances were achieved for both training and testing phases. (C) 2014 by National Stroke Association