Yazar "Karakuzu, Cihan" seçeneğine göre listele

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    Epilepsy diagnosis using artificial neural network learned by PSO
    (2015) Yalçın, Nesibe; Karakuzu, Cihan; Tezel, Gülay
    Abstract: Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.
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    Epilepsy diagnosis using artificial neural network learned by PSO
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2015) Yalcin, Nesibe; Tezel, Gulay; Karakuzu, Cihan
    Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to PSO versions, and 7 PSO-based neural network models are described. Among these models, PSONN3 and PSONN4 are determined to be appropriate models for epilepsy diagnosis due to having better classification accuracy. The training methods-based PSO versions are compared with the backpropagation algorithm, which is a traditional method. In addition, different numbers of neurons, iterations/generations, and swarm sizes have been considered and tried. Results obtained from the models are evaluated, interpreted, and compared with the results of earlier works done with the same dataset in the literature.
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    Epilepsy Diagnosis Using Artificial Neural Network Learned by PSO -- 2
    (TURGUT OZAL UNIV, 2012) Yalcin, Nesibe; Tezel, Gulay; Karakuzu, Cihan
    In this paper, epilepsy diagnosis has been investigated by using Electroencephalogram (EEG) records. For this purpose, a technique as the classifier Artificial Neural Networks (ANN), which is frequently used and known as an active classification technique, is used. Particle Swarm Optimization (PSO) method is preferred as training algorithm for ANN. PSO based neural network model (PSONN) is diversified according to PSO variants and seven PSO based neural network models are described. In these models, PSONN3 and PSONN4 are determined as appropriate models for the classification. In addition, different number of neurons, iterations/generations and swarm sizes have been considered and tried. Obtained results of the models have been evaluated.
  • Küçük Resim Yok
    Öğe
    Epilepsy Diagnosis Using PSO based ANN
    (ALIFE ROBOTICS CO, LTD, 2013) Yalcin, Nesibe; Karakuzu, Cihan; Tezel, Gulay
    Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in classification of epileptic attacks and epilepsy diagnosis. In this paper, epilepsy diagnosis by evaluation of EEG records is presented. Artificial Neural Networks (ANN) is used as a classification technique. Particle Swarm Optimization (PSO) method, which doesn't require gradient calculation, derivative information and any solution of differential equations is preferred for ANN training. This training method is compared with back propagation algorithm, which is one of the traditional methods, and the results are interpreted. In case of using the PSO algorithm, the training and test classification accuracies are %99.67 and %100, respectively. PSO based neural network model (PSONN) has a better classification accuracy than back propagation neural network model (BPNN) for epilepsy diagnosis.