Papers by Abdulkadir Sengur
In the last decades, several tools and various methodologies have been proposed by the researcher... more In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians.
In this paper, a comparative study of classification of the analog modulated communication signal... more In this paper, a comparative study of classification of the analog modulated communication signals using clustering techniques is introduced. Four different clustering algorithms are implemented for classifying the analog signals. These clustering techniques are K-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. Two key features are used for characterizing the analog modulation types. Performance comparison of these clustering algorithms is made using computer simulations.
Renewable Energy, 2008
This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP... more This paper reports on a modeling study of ground coupled heat pump (GCHP) system performance (COP) by using a support vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As regards the SVM, it has a superior capability for generalization, and this capability is independent of the dimensionality of the
Journal of Electronic Imaging, 2013
Expert Systems with Applications, 2008
The use of artificial intelligence methods in medical analysis is increasing. This is mainly beca... more The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.
Expert Systems with Applications, 2010
In this work, we investigate the use of ensemble learning for improving classifiers which is one ... more In this work, we investigate the use of ensemble learning for improving classifiers which is one of the important directions in the current research of machine learning, in which bagging, boosting and random subspace are three powerful and popular representatives. Researchers have so far shown the efficacies of ensemble methods in many practical classification problems. However, for valvular heart disease detection, there are almost no studies investigating their feasibilities. Thus, in this study, we evaluate the performance of ...
Expert Systems with Applications, 2009
This study introduces the usage of multiclass least-squares support vector machines (MC-LS-SVM) f... more This study introduces the usage of multiclass least-squares support vector machines (MC-LS-SVM) for classification purposes of the analog modulated communication signals. Fulfilled study uses our previous papers where ANN and clustering methods were used as classifiers and several key features which were extracted from the instantaneous properties of the intercepted signal for characterizing the modulation types. k-fold cross-validation test, classification accuracy and confusion matrix methods are used for calculating the performance of the MC-LS-SVM classifier. Moreover, the performance of the MC-LS-SVM is compared with our previous studies where ANN and clustering efforts for modulation classification were investigated. According to the computer simulations, 100% correct classification rate was obtained when 10-fold cross-validation test method was used.
Expert Systems with Applications, 2008
... This paper investigates the usage of Wavelet transform (WT) and Adaptive neuro-fuzzy inferenc... more ... This paper investigates the usage of Wavelet transform (WT) and Adaptive neuro-fuzzy inference system (ANFIS) for color texture classification problem. ... The ANFIS is a fuzzy Sugeno model put in the framework of adaptive systems to facilitate learning and adaptation ...
Expert Systems with Applications, 2009
This paper reports on a modelling study of new solar air heater (SAH) system efficiency by using ... more This paper reports on a modelling study of new solar air heater (SAH) system efficiency by using leastsquares support vector machine (LS-SVM) method. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the LS-SVM, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data. In this study, a LS-SVM based method was intended to adopt SAH system for efficient modelling. For modelling, different mass flow rates in flow duct and collector types are used and then for obtaining the optimum LS-SVM parameters, such as regularization parameter, and optimum kernel function and parameters, several tests have been carried out. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that root mean squared error (RMSE) value is 0.0024, the coefficient of multiple determinations (R 2 ) value is 0.9997 and coefficient of variation (cov) value is 2.1194 for the proposed radial basis function (RBF)-kernel LS-SVM method at 0.03 kg/s air mass flow rate. It is found that RMSE value is 0.0135, R 2 value is 0.9991 and cov value is 2.9868 for the proposed RBF-kernel LS-SVM method at 0.05 kg/s air mass flow rate. Comparison between predicted and experimental results indicates that the proposed LS-SVM model can be used for estimating the efficiency of SAHs with reasonable accuracy.
Building and Environment, 2008
The goal of this work is to predict the daily performance (COP) of a ground-source heat pump (GSH... more The goal of this work is to predict the daily performance (COP) of a ground-source heat pump (GSHP) system with the minimum data set based on an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy weighted pre-processing (FWP) method. To evaluate the effectiveness of our proposal (FWP-ANFIS), a computer simulation is developed on MATLAB environment. The comparison of the proposed hybridized system's results with the standard ANFIS results is carried out and the results are given in the tables. The efficiency of the proposed method was demonstrated by using the 3-fold cross-validation test. The statistical methods, such as the root-mean squared (RMS), the coefficient of multiple determinations (R 2 ) and the coefficient of variation (cov), are given to compare the predicted and actual values for model validation. The average R 2 values is 0.9998, the average RMS value is 0.0272 and the average cov value is 0.7733, which can be considered as very promising. The data set for the COP of GSHP system available included 38 data patterns. The simulation results show that the FWP-based ANFIS can be used in an alternative way in these systems. The prediction results of the proposed structure were much better than the standard ANFIS results. Therefore, instead of limited experimental data found in the literature, faster and simpler solutions are obtained using hybridized structures such as FWP-based ANFIS. r
Expert systems with applications, May 31, 2009
In the last decades, several tools and various methodologies have been proposed by the researcher... more In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software ...
Journal of Applied Sciences, 2005
In this study, an interface for automatic classification of the analog modulated communication si... more In this study, an interface for automatic classification of the analog modulated communication signal is introduced. An educational approach is adopted here to present an interface which can be used as a tool for education and demonstrative purposes. While emphasis has been placed on the designed interface, the certain type of the analog modulated communication signals can also be recognized
k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parame... more k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as U = T + I − F. A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method's performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.
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Papers by Abdulkadir Sengur