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Laktasi, Bosnia and Herzegovina

International Burch University was established in 2008 in Sarajevo, capital of Bosnia and Herzegovina, with the goal of presenting a unique opportunity to rethink the very idea of a modern university and formulate a blueprint for the future. Upon the Sarajevo Canton Ministry of Education decision, teaching process was started according to Bologna System of Education entirely in English language.IBU is member of the private Bosna Sema Educational Institutions family, well known on the ground of Bosnia and Herzegovina for the success its students shows participating various educational competitions on federal, country and international level. Bosna Sema Educational Institutions offers the education in seven schools from primary school, through college up to university in cities of Tuzla, Bihac, Zenica and Sarajevo.IBU, like other institutions owned by the Bosna Sema Educational Institutions family, is part of the Gülen movement. Wikipedia.


Subasi A.,International BURCH University
Signal, Image and Video Processing | Year: 2013

Support vector machines (SVMs) have been widely used in many pattern recognition problems. Generally, the performance of SVM classifiers is affected by the selection of the kernel parameters. However, SVM does not offer the mechanism for proper setting of their control parameters. The objective of this research is to optimize the parameters without degrading the SVM classification accuracy in diagnosis of neuromuscular disorders. An evolutionary approach for designing an SVM-based classifier (ESVM) by optimization of automatic parameter tuning using genetic algorithm is proposed. To illustrate and evaluate the efficiency of ESVM, a typical application to EMG signals classification using normal, myopathic, and neurogenic datasets is adopted. In the proposed method, the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT), and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. It is shown that ESVM can obtain a high accuracy of 97 % using tenfold cross-validation for the EMG datasets. ESVM is developed as an efficient tool, so that various SVMs can be used conveniently as the core of ESVM for diagnosis of neuromuscular disorders. © 2013, Springer-Verlag London. Source


Subasi A.,International BURCH University | Gursoy M.I.,Adiyaman University
Expert Systems with Applications | Year: 2010

In this work, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using DWT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminant analysis (LDA) is used to reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM) with two discrete outputs: epileptic seizure or not. The performance of classification process due to different methods is presented and compared to show the excellent of classification process. These findings are presented as an example of a method for training, and testing a seizure prediction method on data from individual petit mal epileptic patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical operation. © 2010 Elsevier Ltd. All rights reserved. Source


Yagcitekin B.,Yildiz Technical University | Uzunoglu M.,Yildiz Technical University | Uzunoglu M.,International BURCH University
Applied Energy | Year: 2015

This paper presents a double-layer smart charging management algorithm (SCMA) for electric vehicles in working place parking lots considering smart grid concept. This model enables safe and controlled charging, which satisfies both power grid and drivers' needs comprehensively. First level of SCMA controls each transformer power demand, transformer capacity, charging station status and the shortest way to reach suitable charger. Second level of SCMA is used during the charging process in order to provide cost-effective and reliable charging as well as less impact on power grid. The proposed SCMA strategy successfully routes the electric vehicles (EVs) to the most suitable charging point, decreases the charging costs and prevents the overloading of transformers. In this study, a comparison regarding the impacts of the whole charging process of the EVs on the power grid and drivers' requests with/without the proposed SCMA is presented and the results are discussed in detail. © 2015 Elsevier Ltd. Source


Subasi A.,International BURCH University | Yilmaz A.S.,Kahramanmaras Sutcu Imam University | Tufan K.,Fatih University
Energy Conversion and Management | Year: 2011

In this paper, a novel algorithm, based on the Teager Energy Operator (TEO) of a sinusoidal waveform, proposed to detect and analyze the voltage disturbances. Most common power quality (PQ) disturbances at the distribution level such as voltage sags, notches, and capacitor switching are presented. These examples provide the basis for further characterization of other power quality events. Magnitudes of transient PQ events are located in the width of the signal. Furthermore, meaningful components of transients are analyzed. The whole method is implemented and tested over a sample representing recorded disturbances. Simulation and experimental results of different PQ disturbances show that the proposed method is fast and robust in detecting voltage disturbances and requires only a few samples to calculate the energy of a signal. © 2010 Elsevier Ltd. All rights reserved. Source


Subasi A.,International BURCH University
Computers in Biology and Medicine | Year: 2013

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. © 2013 Elsevier Ltd. Source

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