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Full-Text Articles in Physical Sciences and Mathematics
A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş
A Penalty Function Method For Designing Efficient Robust Classifiers With Input Space Optimal Separating Surfaces, Ayşegül Uçar, Yakup Demi̇r, Cüneyt Güzeli̇ş
Turkish Journal of Electrical Engineering and Computer Sciences
This paper considers robust classification as a constrained optimization problem. Where the constraints are nonlinear, inequalities defining separating surfaces, whose half spaces include or exclude the data depending on their classes and the cost, are used for attaining robustness and providing the minimum volume regions specified by the half spaces of the surfaces. The constraints are added to the cost using penalty functions to get an unconstrained problem for which the gradient descent method can be used. The separating surfaces, which are aimed to be found in this way, are optimal in the input data space in contrast to the …
Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu
Feature Selection On Single-Lead Ecg For Obstructive Sleep Apnea Diagnosis, Hüseyi̇n Gürüler, Mesut Şahi̇n, Abdullah Feri̇koğlu
Turkish Journal of Electrical Engineering and Computer Sciences
Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram. Although high accuracies have been achieved in detection of apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to the time domain, frequency domain, and nonlinear calculations of heart rate variability analysis, and then proposes a method of feature selection based on correlation matrices (CMs). The results show that the CMs can be utilized in …
Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya
Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository's web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier's performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and …
A Rule Induction Algorithm For Knowledge Discovery And Classification, Ömer Akgöbek
A Rule Induction Algorithm For Knowledge Discovery And Classification, Ömer Akgöbek
Turkish Journal of Electrical Engineering and Computer Sciences
Classification and rule induction are key topics in the fields of decision making and knowledge discovery. The objective of this study is to present a new algorithm developed for automatic knowledge acquisition in data mining. The proposed algorithm has been named RES-2 (Rule Extraction System). It aims at eliminating the pitfalls and disadvantages of the techniques and algorithms currently in use. The proposed algorithm makes use of the direct rule extraction approach, rather than the decision tree. For this purpose, it uses a set of examples to induce general rules. In this study, 15 datasets consisting of multiclass values with …
Hybrid Of Genetic Algorithm And Great Deluge Algorithm For Rough Set Attribute Reduction, Najmeh Sadat Jaddi, Salwani Abdullah
Hybrid Of Genetic Algorithm And Great Deluge Algorithm For Rough Set Attribute Reduction, Najmeh Sadat Jaddi, Salwani Abdullah
Turkish Journal of Electrical Engineering and Computer Sciences
The attribute reduction problem is the process of reducing unimportant attributes from a decision system to decrease the difficulty of data mining or knowledge discovery tasks. Many algorithms have been used to optimize this problem in rough set theory. The genetic algorithm (GA) is one of the algorithms that has already been applied to optimize this problem. This paper proposes 2 kinds of memetic algorithms, which are a hybridization of the GA, with 2 versions (linear and nonlinear) of the great deluge (GD) algorithm. The purpose of this hybridization is to investigate the ability of this local search algorithm to …
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Turkish Journal of Electrical Engineering and Computer Sciences
A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis …
Measuring Traffic Flow And Classifying Vehicle Types: A Surveillance Video Based Approach, Erhan İnce
Measuring Traffic Flow And Classifying Vehicle Types: A Surveillance Video Based Approach, Erhan İnce
Turkish Journal of Electrical Engineering and Computer Sciences
The paper presents a vehicle counting method based on invariant moments and shadow aware foreground masks. Estimation of the background and the segmentation of foreground regions can be done using either the Mixture of Gaussians model (MoG) or an improved version of the Group Based Histogram (GBH) technique. The work demonstrates that, even though the improved GBH method delivers performance just as good as MoG, considering computational efficiency, MoG is more suitable. Shadow aware binary masks for each frame are formed by using background subtraction and shadow removal in the Hue Saturation and Value (HSV) domain. To determine new vehicles …
A New Approach Using Temporal Radial Basis Function In Chronological Series, Mustapha Guezouri
A New Approach Using Temporal Radial Basis Function In Chronological Series, Mustapha Guezouri
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we present an extended form of Radial Basis Function network called Temporal-RBF (T-RBF) network. This extended network can be used in decision rules and classification in Spatio-Temporal domain applications, like speech recognition, economic fluctuations, seismic measurements and robotics applications. We found that such a network complies with relative ease to constraints such as capacity of universal approximation, sensibility of node, local generalisation in receptive field, etc. For an optimal solution based on a probabilistic approach with a minimum of complexity, we propose two TRBF models (1 and 2). Application to the problem of Mackey-Glass time series has …
Real-Time Classification Algorithm For Recognition Of Machine Operating Modes By Use Of Self-Organizing Maps, Gancho Vachkov, Yuhiko Kiyota, Koji Komatsu, Satoshi Fujii
Real-Time Classification Algorithm For Recognition Of Machine Operating Modes By Use Of Self-Organizing Maps, Gancho Vachkov, Yuhiko Kiyota, Koji Komatsu, Satoshi Fujii
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper a new algorithm for classification and real-time recognition of different a-priorily assumed operating modes for construction machines is proposed. This algorithm utilizes the effectiveness of the Self-Organizing Maps (SOM) for creating the so called Separation Models, that are able to distinguish each operating mode separately. After training, these models are used in a real-time procedure, which calculates at each sampling time the minimal Euclidean distances from the current data point to a certain node of each SOM. Then the separation model (represented by a respective SOM) that has the least minimal distance to this data point defines …
Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik
Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik
Turkish Journal of Electrical Engineering and Computer Sciences
The aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An artificial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of $a_1, a_2, a_3, a_4$ and their signal power obtained from different arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.