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Papers by Musa PEKER

Research paper thumbnail of An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms

Research paper thumbnail of Predicting and Analyzing Students’ Performance: An Educational Data Mining Approach

Proceedings of the 2016 Sixth International Conference on Advanced Computing & Communication Technologies, 2016

Research paper thumbnail of A new complex-valued intelligent system for automated epilepsy diagnosis using EEG signals

Research paper thumbnail of A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM

Journal of Medical Systems, 2016

Research paper thumbnail of A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform

Computer Methods and Programs in Biomedicine, 2016

Research paper thumbnail of Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Neural Computing and Applications, 2016

Research paper thumbnail of A Real-Time and Motion-Sensitive Security Application with Face Recognition

Research paper thumbnail of article1380536591 Peker and Zengin

Research paper thumbnail of A software tool for complex-valued neural network: CV-ANN

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015

Research paper thumbnail of Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

Journal of Healthcare Engineering, 2015

Parkinson's disease (PD) is a neu... more Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.

Research paper thumbnail of Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach

Electronic Journal of Biotechnology, 2015

Research paper thumbnail of A novel hybrid method for determining the depth of anesthesia level: Combining ReliefF feature selection and random forest algorithm (ReliefF+RF)

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015

Research paper thumbnail of A comparison of different classification algorithms for determining the depth of anesthesia level on a new set of attributes

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015

Research paper thumbnail of A Novel Method for Automated Diagnosis of Epilepsy using Complex-Valued Classifiers

IEEE journal of biomedical and health informatics, Jan 6, 2015

The study reported herein proposes a new method for the diagnosis of epilepsy from electroencepha... more The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out the study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation (DTCWT) at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements-maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks (CVANN). The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity and specificity. The proposed method is tested using a benchmark EEG dataset and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epil...

Research paper thumbnail of Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks

Journal of medical systems, 2015

The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classificati... more The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed pa...

Research paper thumbnail of Determining the Appropriate Amount of Anesthetic Gas Using DWT and EMD Combined with Neural Network

Journal of Medical Systems, 2014

Research paper thumbnail of Real-Time Face Recognition With Artificial Neural Network Trained by Particle Swarm Optimization

Research paper thumbnail of An efficient color detection in RGB space using hierarchical neural network structure

2011 International Symposium on Innovations in Intelligent Systems and Applications, 2011

Abstract Color detection is generally a primary stage in most of the image processing application... more Abstract Color detection is generally a primary stage in most of the image processing application, if the application is based on the color information, such as road sign detection, face detection, skin color detection, object detection and object tracking etc. As the ...

Research paper thumbnail of i-EEG: A Software Tool for EEG Feature Extraction, Feature Selection and Classification

Research paper thumbnail of An efficient approach based on mrMR feature selection and decision tree for automatic sleep stage scoring

Research paper thumbnail of An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms

Research paper thumbnail of Predicting and Analyzing Students’ Performance: An Educational Data Mining Approach

Proceedings of the 2016 Sixth International Conference on Advanced Computing & Communication Technologies, 2016

Research paper thumbnail of A new complex-valued intelligent system for automated epilepsy diagnosis using EEG signals

Research paper thumbnail of A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM

Journal of Medical Systems, 2016

Research paper thumbnail of A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform

Computer Methods and Programs in Biomedicine, 2016

Research paper thumbnail of Signal detection based on empirical mode decomposition and Teager–Kaiser energy operator and its application to P and S wave arrival time detection in seismic signal analysis

Neural Computing and Applications, 2016

Research paper thumbnail of A Real-Time and Motion-Sensitive Security Application with Face Recognition

Research paper thumbnail of article1380536591 Peker and Zengin

Research paper thumbnail of A software tool for complex-valued neural network: CV-ANN

2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015

Research paper thumbnail of Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

Journal of Healthcare Engineering, 2015

Parkinson's disease (PD) is a neu... more Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.

Research paper thumbnail of Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach

Electronic Journal of Biotechnology, 2015

Research paper thumbnail of A novel hybrid method for determining the depth of anesthesia level: Combining ReliefF feature selection and random forest algorithm (ReliefF+RF)

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015

Research paper thumbnail of A comparison of different classification algorithms for determining the depth of anesthesia level on a new set of attributes

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), 2015

Research paper thumbnail of A Novel Method for Automated Diagnosis of Epilepsy using Complex-Valued Classifiers

IEEE journal of biomedical and health informatics, Jan 6, 2015

The study reported herein proposes a new method for the diagnosis of epilepsy from electroencepha... more The study reported herein proposes a new method for the diagnosis of epilepsy from electroencephalography (EEG) signals based on complex classifiers. To carry out the study, first the features of EEG data are extracted using a dual-tree complex wavelet transformation (DTCWT) at different levels of granularity to obtain size reduction. In subsequent phases, five features (based on statistical measurements-maximum value, minimum value, arithmetic mean, standard deviation, median value) are obtained by using the feature vectors, and are presented as the input dimension to the complex-valued neural networks (CVANN). The evaluation of the proposed method is conducted using the k-fold cross-validation methodology, reporting on classification accuracy, sensitivity and specificity. The proposed method is tested using a benchmark EEG dataset and high accuracy rates were obtained. The stated results show that the proposed method can be used to design an accurate classification system for epil...

Research paper thumbnail of Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks

Journal of medical systems, 2015

The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classificati... more The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed pa...

Research paper thumbnail of Determining the Appropriate Amount of Anesthetic Gas Using DWT and EMD Combined with Neural Network

Journal of Medical Systems, 2014

Research paper thumbnail of Real-Time Face Recognition With Artificial Neural Network Trained by Particle Swarm Optimization

Research paper thumbnail of An efficient color detection in RGB space using hierarchical neural network structure

2011 International Symposium on Innovations in Intelligent Systems and Applications, 2011

Abstract Color detection is generally a primary stage in most of the image processing application... more Abstract Color detection is generally a primary stage in most of the image processing application, if the application is based on the color information, such as road sign detection, face detection, skin color detection, object detection and object tracking etc. As the ...

Research paper thumbnail of i-EEG: A Software Tool for EEG Feature Extraction, Feature Selection and Classification

Research paper thumbnail of An efficient approach based on mrMR feature selection and decision tree for automatic sleep stage scoring

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