Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Publication Year
- Publication
-
- Turkish Journal of Electrical Engineering and Computer Sciences (65)
- SMU Data Science Review (5)
- Karbala International Journal of Modern Science (2)
- Applied Mathematics & Information Sciences (1)
- Elinvo (Electronics, Informatics, and Vocational Education) (1)
-
- Future Computing and Informatics Journal (1)
- Journal of International Technology and Information Management (1)
- Journal of Spatial Information Science (1)
- Journal of System Simulation (1)
- Journal of the Arkansas Academy of Science (1)
- School of Computer Science & Engineering Faculty Publications (1)
Articles 61 - 80 of 80
Full-Text Articles in Physical Sciences and Mathematics
Multiclass Semantic Segmentation Of Faces Using Crfs, Khalil Khan, Nasir Ahmad, Khalil Ullah, Irfanud Din
Multiclass Semantic Segmentation Of Faces Using Crfs, Khalil Khan, Nasir Ahmad, Khalil Ullah, Irfanud Din
Turkish Journal of Electrical Engineering and Computer Sciences
Multiclass semantic image segmentation is widely used in a variety of computer vision tasks, such as object segmentation and complex scene understanding. As it decomposes an image into semantically relevant regions, it can be applied in segmentation of face images. In this paper, an algorithm based on multiclass semantic segmentation of faces is proposed using conditional random fields. In the proposed model, each node corresponds to a superpixel, while the neighboring superpixels are connected to nodes through edges. Unlike previous approaches, which rely on three or four classes, the label set is extended here to six classes, i.e. hair, eyes, …
Classification Of Eeg Signals Of Familiar And Unfamiliar Face Stimuli Exploiting Most Discriminative Channels, Abdurrahman Özbeyaz, Sami̇ Arica
Classification Of Eeg Signals Of Familiar And Unfamiliar Face Stimuli Exploiting Most Discriminative Channels, Abdurrahman Özbeyaz, Sami̇ Arica
Turkish Journal of Electrical Engineering and Computer Sciences
The objective of the study is to classify electroencephalogram signals recorded in a familiar and unfamiliar face recognition experiment. Frontal views of familiar and unfamiliar face images were shown to 10 volunteers in different sessions. In contrast to previous studies, no marker button was used during the experiment. Participants had to decide whether the displayed face was familiar or unfamiliar at the instant of stimulus presentation. The signals were analyzed in the preprocessing, channel selection, feature extraction, and classification stages. The novel two-feature extraction and eight-channel selection methods were applied to the analyses. Sixteen classification results were compared and the …
Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant
Dtreesim: A New Approach To Compute Decision Tree Similarity Using Re-Mining, Gözde Bakirli, Derya Bi̇rant
Turkish Journal of Electrical Engineering and Computer Sciences
A number of recent studies have used a decision tree approach as a data mining technique; some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. The main objective of this study is to compute the similarity of decision trees using data mining techniques. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and …
Integration Of Spectral And Spatial Information Via Local Covariance Matrices For Segmentation And Classification Of Hyperspectral Images, Uğur Ergül, Gökhan Bi̇lgi̇n
Integration Of Spectral And Spatial Information Via Local Covariance Matrices For Segmentation And Classification Of Hyperspectral Images, Uğur Ergül, Gökhan Bi̇lgi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
In this work, a novel approach is presented for the feature extraction step in hyperspectral image processing to form more discriminative features between different pixel regions. The proposed method combines both spatial and spectral information, which is very important for segmentation and classification of hyperspectral images. For comparison, five different feature sets are formed using eigen decomposition of local covariance matrices of subcubes located around a pixel of interest in the scene. Subcubes of neighbor pixels are obtained by a windowed structure to expose pattern similarities. As a novel approach, local covariance matrices are computed in eigenspace and proposed feature …
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
Turkish Journal of Electrical Engineering and Computer Sciences
Face recognition is an essential issue in modern-day applications since it can be used in many areas for several purposes. Many methods have been proposed for face recognition. It is a difficult task since variations in lighting, instantaneous mimic varieties, posing angles, and scaling differences can drastically change the appearance of the face. To suppress these complications, effective feature extraction and proper alignment of face images gain as much importance as the recognition method choice. In this paper, we provide an extensive comparison of the state-of-the-art face recognition methods with the most well-known techniques used in feature representation. In order …
A New Fuzzy Membership Assignment And Model Selection Approach Based On Dynamic Class Centers For Fuzzy Svm Family Using The Firefly Algorithm, Omid Naghash Almasi, Modjtaba Rouhani
A New Fuzzy Membership Assignment And Model Selection Approach Based On Dynamic Class Centers For Fuzzy Svm Family Using The Firefly Algorithm, Omid Naghash Almasi, Modjtaba Rouhani
Turkish Journal of Electrical Engineering and Computer Sciences
The support vector machine (SVM) is a powerful tool for classification problems. Unfortunately, the training phase of the SVM is highly sensitive to noises in the training set. Noises are inevitable in real-world applications. To overcome this problem, the SVM was extended to a fuzzy SVM by assigning an appropriate fuzzy membership to each data point. However, suitable choice of fuzzy memberships and an accurate model selection raise fundamental issues. In this paper, we propose a new method based on optimization methods to simultaneously generate appropriate fuzzy membership and solve the model selection problem for the SVM family in linear/nonlinear …
A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman
A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman
Turkish Journal of Electrical Engineering and Computer Sciences
Early diagnosis of dangerous heart conditions is very important for the treatment of heart diseases and for the prevention of sudden cardiac death. Automatic electrocardiogram (ECG) arrhythmia classifiers are essential to timely diagnosis. However, most of the medical diagnosis systems proposed in the literature are software-based. This work focused on the hardware implementation of a mobile artificial neural network (ANN)-based arrhythmia classifier that is implemented on a field programmable gate array (FPGA) as a single chip solution, as an alternative to various software models of ANNs. Due to the parallel nature of ANNs, hardware implementation of ANNs needs a large …
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 …
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
Turkish Journal of Electrical Engineering and Computer Sciences
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …
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 …
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 …
Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan
Online Feature Selection And Classification With Incomplete Data, Habi̇l Kalkan
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a classification system in which learning, feature selection, and classification for incomplete data are simultaneously carried out in an online manner. Learning is conducted on a predefined model including the class-dependent mean vectors and correlation coefficients, which are obtained by incrementally processing the incoming observations with missing features. A nearest neighbor with a Gaussian mixture model, whose parameters are also estimated from the trained model, is used for classification. When a testing observation is received, the algorithm discards the missing attributes on the observation and ranks the available features by performing feature selection on the model that …
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 …
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 …
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 …
Learning Multiple Languages In Groups, Sanjay Jain, Efim Kinber
Learning Multiple Languages In Groups, Sanjay Jain, Efim Kinber
School of Computer Science & Engineering Faculty Publications
We consider a variant of Gold’s learning paradigm where a learner receives as input different languages (in the form of one text where all input languages are interleaved). Our goal is to explore the situation when a more “coarse” classification of input languages is possible, whereas more refined classification is not. More specifically, we answer the following question: under which conditions, a learner, being fed different languages, can produce grammars covering all input languages, but cannot produce grammars covering input languages for any . We also consider a variant of this task, where each of the output grammars may not …
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%.