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Articles 31 - 54 of 54
Full-Text Articles in Computer Engineering
A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George
A Robust Ensemble Feature Selector Based On Rank Aggregation For Developing New Vo\Textsubscript{2}Max Prediction Models Using Support Vector Machines, Fatih Abut, Mehmet Fati̇h Akay, James George
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new ensemble feature selector, called the majority voting feature selector (MVFS), for developing new maximal oxygen uptake (VO2max) prediction models using a support vector machine (SVM). The approach is based on rank aggregation, which meaningfully utilizes the correlation among the relevance ranks of predictor variables given by three state-of-the-art feature selectors: Relief-F, minimum redundancy maximum relevance (mRMR), and maximum likelihood feature selection (MLFS). By applying the SVM combined with MVFS on a self-created dataset containing maximal and submaximal exercise data from 185 college students, several new hybrid (VO2max) prediction models have been created. To compare the …
Speech Emotion Recognition Using Semi-Nmf Feature Optimization, Surekha Reddy Bandela, T Kishore Kumar
Speech Emotion Recognition Using Semi-Nmf Feature Optimization, Surekha Reddy Bandela, T Kishore Kumar
Turkish Journal of Electrical Engineering and Computer Sciences
In recent times, much research is progressing forward in the field of speech emotion recognition (SER). Many SER systems have been developed by combining different speech features to improve their performances. As a result, the complexity of the classifier increases to train this huge feature set. Additionally, some of the features could be irrelevant in emotion detection and this leads to a decrease in the emotion recognition accuracy. To overcome this drawback, feature optimization can be performed on the feature sets to obtain the most desirable emotional feature set before classifying the features. In this paper, semi-nonnegative matrix factorization (semi-NMF) …
Application Of Multiscale Fuzzy Entropy Features For Multilevel Subject-Dependent Emotion Recognition, Hamzah Lotfalinezhad, Ali Maleki
Application Of Multiscale Fuzzy Entropy Features For Multilevel Subject-Dependent Emotion Recognition, Hamzah Lotfalinezhad, Ali Maleki
Turkish Journal of Electrical Engineering and Computer Sciences
Emotion recognition can be used in clinical and nonclinical situations. Despite previous works which mostly used time and frequency features of electroencephalogram (EEG) signals in subject-dependent emotion recognition issues, we used multiscale fuzzy entropy as a nonlinear dynamic feature. The EEG signals of the well-known Database for Emotion Analysis Using Physiological signals dataset was used for classification of two and three levels of emotions in arousal and valence space. The compound feature selection with a cost of average accuracy of support vector machine classifier was used to reduce feature dimensions. For subject-dependent systems, the proposed method is superior in comparison …
Performance Comparison Of Support Vector Machine, Random Forest, And Extreme Learning Machine For Intrusion Detection, Iftikhar Ahmad, Muhammad Javed Iqbal, Mohammad Basheri, Aneel Rahim
Performance Comparison Of Support Vector Machine, Random Forest, And Extreme Learning Machine For Intrusion Detection, Iftikhar Ahmad, Muhammad Javed Iqbal, Mohammad Basheri, Aneel Rahim
Articles
Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used …
Extended Correlated Principal Component Analysis With Svm-Puk In Opinion Mining, Kollimarla Anusha Devi, Deepak Chowdary Edara, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Extended Correlated Principal Component Analysis With Svm-Puk In Opinion Mining, Kollimarla Anusha Devi, Deepak Chowdary Edara, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli
Turkish Journal of Electrical Engineering and Computer Sciences
With the rapid growth of microblogs and online sites, an inordinate number of product reviews are available on the Internet. They not only help in analyzing, but also assist in making informed decisions about product quality. In the proposed work, an extended correlated principal component analysis (ECPCA) is used for dimensionality reduction. A comparative analysis is conducted on movie reviews (DB-1) and Twitter datasets (DB-2 and DB-3) in opinion mining extraction. The performance of naive Bayes, CHIRP, and support vector machine (SVM) with kernel methods such as radial basis function (RBF), polynomial, and Pearson (PUK) are compared and analyzed on …
Classification And Regression Analysis Using Support Vector Machine For Classifying And Locating Faults In A Distribution System, Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Bin Illias
Classification And Regression Analysis Using Support Vector Machine For Classifying And Locating Faults In A Distribution System, Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Bin Illias
Turkish Journal of Electrical Engineering and Computer Sciences
Various fault location methods have been developed in the past to identify the faulty phase, fault type, faulty section, and distance. However, this identification is commonly conducted in a separate manner. An effective fault location should be able to identify all of these at the same time. Therefore, in this work, a method using a support vector machine (SVM) to identify the fault type, faulty section, and distance considering the faulty phase is proposed. The proposed method uses voltage sag magnitude of the distribution system as the main feature for the SVM to identify faults. The fault type is classified …
Classification Of Surface Electromyogram Signals Based On Directed Acyclic Graphs And Support Vector Machines, Xinhui Hu, Jiangming Kan, Wenbin Li
Classification Of Surface Electromyogram Signals Based On Directed Acyclic Graphs And Support Vector Machines, Xinhui Hu, Jiangming Kan, Wenbin Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents a novel classification approach for surface electromyogram (sEMG) signals. The proposed classification approach involves two steps: (1) feature extraction from an sEMG, in which a 7-dimensional feature vector is extracted from 27 types of features of the sEMG by linear discriminant analysis (LDA), and (2) a novel classifier, DAGSVMerr, based on a directed acyclic graph (DAG) and support vector machine (SVM), in which a separability measure function based on erroneous recognition rates (ERRs) is defined to determine the initial operation list. The proposed approach takes advantage of the feedback idea to improve the performance of the classification. …
Compact Local Gabor Directional Number Pattern For Facial Expression Recognition, Zhengyan Zhang, Guanming Lu, Jingjie Yan, Haibo Li, Ning Sun, Xia Li
Compact Local Gabor Directional Number Pattern For Facial Expression Recognition, Zhengyan Zhang, Guanming Lu, Jingjie Yan, Haibo Li, Ning Sun, Xia Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper explores a novel method to represent face images for facial expression recognition; it is named compact local Gabor directional number pattern (CLGDNP). By convolving the face images with Gabor filters, we encode the magnitude and phase response images in each scale, and calculate the histograms in several nonoverlapping regions of each encoded image. Finally, we obtain two spatial histogram sequences by the aid of the mean pooling technology and concatenate them to form the facial descriptor. Moreover, for evaluating the performance of the proposed method, we employ a support vector machine to conduct some extensive classification experiments on …
An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo
An Ameliorated Prediction Of Drug–Target Interactions Based On Multi-Scale Discrete Wavelet Transform And Network Features, Cong Shen, Yijie Ding, Jijun Tang, Xinying Xu, Fei Guo
Faculty Publications
The prediction of drug–target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper, we propose a kind of drug–target interactions predictor adopting multi-scale discrete wavelet transform and network features (named as DAWN) in order to solve the DTIs prediction problem. We encode the drug molecule by a substructure fingerprint with a dictionary of substructure patterns. Simultaneously, we apply the discrete wavelet transform (DWT) to extract features from target sequences. Then, we concatenate and normalize the target, drug, …
Generalized Referenceless Image Quality Assessment Framework Using Texture Energy Measures And Pattern Strength Features, Jayashri Bagade, Kulbir Singh, Yogesh Dandawate
Generalized Referenceless Image Quality Assessment Framework Using Texture Energy Measures And Pattern Strength Features, Jayashri Bagade, Kulbir Singh, Yogesh Dandawate
Turkish Journal of Electrical Engineering and Computer Sciences
Referenceless image quality assessment is a challenging and critical problem in today's multimedia applica\-tions. Texture patterns in images are normally at high frequencies compared to lower ones. Due to the effect of distortions during acquisition, compression, and transmission, texture deviation artifacts are generated that cause a granular effect in the image. Other artifacts, such as blocking, affect high frequencies in an image, causing distorted edges. Combining the analysis of texture deviation and other artifacts helps in determining the quality of an image. The proposed approach uses variation in the energy of pixels to quantify the quality of an image. These …
Classifications Of Disturbances Using Wavelet Transform And Support Vector Machine, Neda Hajibandeh, Faramarz Faghihi, Hossein Ranjbar, Hesam Kazari
Classifications Of Disturbances Using Wavelet Transform And Support Vector Machine, Neda Hajibandeh, Faramarz Faghihi, Hossein Ranjbar, Hesam Kazari
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes a new method to detect and classify all kinds of faults, capacitor switching, and load switching in a power system network based on wavelet transform and support vector machines (SVMs). In this regard, a sample of a power system is simulated via MATLAB/Simulink, and by reading the voltage of the point of common coupling and using the wavelet transform, the differences of the outputs of the wavelet transform are investigated. The SVM approach is employed to distinguish the type of the transient (capacitor switching, fault, and/or load switching) in use for the high level outputs of the …
Support Vector Machines For Predicting The Hamstring And Quadriceps Muscle Strength Of College-Aged Athletes, Mehmet Fati̇h Akay, Fati̇h Abut, Ebru Çeti̇n, İmdat Yarim, Boubacar Sow
Support Vector Machines For Predicting The Hamstring And Quadriceps Muscle Strength Of College-Aged Athletes, Mehmet Fati̇h Akay, Fati̇h Abut, Ebru Çeti̇n, İmdat Yarim, Boubacar Sow
Turkish Journal of Electrical Engineering and Computer Sciences
Hamstring and quadriceps muscles are essential for the performance of athletes in various sport branches. Hamstring muscles control running activities and stabilize the knee during turns or tackles, while quadriceps muscles play an important role in jumping and kicking. Although hamstring and quadriceps muscle strength in athletes can be accurately measured using isokinetic dynamometry, practical difficulties, such as the requirement of nonportable and costly equipment as well as a long period of measurement time, motivate the researcher to predict hamstring and quadriceps muscle strength using promising machine-learning methods. The purpose of this study is to build prediction models for estimating …
Breast-Region Segmentation In Mri Using Chest Region Atlas And Svm, Aida Fooladivanda, Shahriar Baradaran Shokouhi, Nasrin Ahmadinejad
Breast-Region Segmentation In Mri Using Chest Region Atlas And Svm, Aida Fooladivanda, Shahriar Baradaran Shokouhi, Nasrin Ahmadinejad
Turkish Journal of Electrical Engineering and Computer Sciences
An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the …
Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker
Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker
Turkish Journal of Electrical Engineering and Computer Sciences
The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation …
A Mapreduce-Based Distributed Svm Algorithm For Binary Classification, Ferhat Özgür Çatak, Mehmet Erdal Balaban
A Mapreduce-Based Distributed Svm Algorithm For Binary Classification, Ferhat Özgür Çatak, Mehmet Erdal Balaban
Turkish Journal of Electrical Engineering and Computer Sciences
Although the support vector machine (SVM) algorithm has a high generalization property for classifying unseen examples after the training phase~and a small loss value, the algorithm is not suitable for real-life classification and regression problems. SVMs cannot solve hundreds of thousands of examples in a training dataset. In previous studies on distributed machine-learning algorithms, the SVM was trained in a costly and preconfigured computer environment. In this research, we present a MapReduce-based distributed parallel SVM training algorithm for binary classification problems. This work shows how to distribute optimization problems over cloud computing systems with the MapReduce technique. In the second …
Fast And De-Noise Support Vector Machine Training Method Based On Fuzzy Clustering Method For Large Real World Datasets, Omid Naghash Almasi, Modjtaba Rouhani
Fast And De-Noise Support Vector Machine Training Method Based On Fuzzy Clustering Method For Large Real World Datasets, Omid Naghash Almasi, Modjtaba Rouhani
Turkish Journal of Electrical Engineering and Computer Sciences
Classifying large and real-world datasets is a challenging problem in machine learning algorithms. Among the machine learning methods, the support vector machine (SVM) is a well-known approach with high generalization ability. Unfortunately, while the number of training data increases and the data contain noise, the performance of SVM significantly decreases. In this paper, a fast and de-noise two-stage method for training SVMs to deal with large, real-world datasets is proposed. In the first stage, data that contain noises or are suspected to be noisy are identified and eliminated from the genuine training dataset. The process of elimination and identification is …
Classification Of Short-Circuit Faults In High-Voltage Energy Transmission Line Using Energy Of Instantaneous Active Power Components-Based Common Vector Approach, Mehmet Yumurtaci, Gökhan Gökmen, Çağri Kocaman, Semi̇h Ergi̇n, Osman Kiliç
Classification Of Short-Circuit Faults In High-Voltage Energy Transmission Line Using Energy Of Instantaneous Active Power Components-Based Common Vector Approach, Mehmet Yumurtaci, Gökhan Gökmen, Çağri Kocaman, Semi̇h Ergi̇n, Osman Kiliç
Turkish Journal of Electrical Engineering and Computer Sciences
The majority of power system faults occur in transmission lines. The classification of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components. Constructed feature …
Impedance Modeling For Classification Of Flavored Green Teas, Munendra Singh, Sunil Semwal, Ashavani Kumar, Shailendra Singh
Impedance Modeling For Classification Of Flavored Green Teas, Munendra Singh, Sunil Semwal, Ashavani Kumar, Shailendra Singh
Turkish Journal of Electrical Engineering and Computer Sciences
This paper proposes an electrical impedance model of flavored green teas. Typically, impedance data of flavored green teas, obtained by electrochemical impedance spectroscopy (EIS), fit into an equivalent circuit that represents the physical and chemical processes taking place in it. The total impedance of each flavor alone is not sufficient, but different values of impedance parameters in the electrical impedance model are responsible for better classification of flavored green teas. Successfully classified data on the basis of their flavors were obtained by different support vector machine (SVM) techniques with encouraging results. The results show that a linear SVM has better …
Intelligent Text Classification System Based On Self-Administered Ontology, Manoj Manuja, Deepak Garg
Intelligent Text Classification System Based On Self-Administered Ontology, Manoj Manuja, Deepak Garg
Turkish Journal of Electrical Engineering and Computer Sciences
Over the last couple of decades, web classification has gradually transitioned from a syntax- to semantic-centered approach that classifies the text based on domain ontologies. These ontologies are either built manually or populated automatically using machine learning techniques. A prerequisite condition to build such systems is the availability of ontology, which may be either full-fledged domain ontology or a seed ontology that can be enriched automatically. This is a dependency condition for any given semantics-based text classification system. We share the details of a proof of concept of a web classification system that is self-governed in terms of ontology population …
Group Control And Identification Of Residential Appliances Using A Nonintrusive Method, Sunil Semwal, Munendra Singh, Rai Sachindra Prasad
Group Control And Identification Of Residential Appliances Using A Nonintrusive Method, Sunil Semwal, Munendra Singh, Rai Sachindra Prasad
Turkish Journal of Electrical Engineering and Computer Sciences
Identifying and controlling (ON/OFF) electrical appliance(s) from a remote location is an essential part of energy management. This motivated us to design a system that can collect the aggregate load signature from a single point, obtain the features, and finally identify the ON state of electrical appliance(s). The proposed disaggregation technique can be divided into two modules: the first part proposes an electrical installation system to disaggregate the appliance at the circuit level, whereas the second part consists of feature selection, dimension reduction, and classification algorithms. Load signatures of electrical appliances were combined with white Gaussian noise to analyze how …
Application Of Hilbert--Huang Transform And Support Vector Machine For Detection And Classification Of Voltage Sag Sources, Alireza Foroughi, Ebrahim Mohammadi, Saeid Esmaeili
Application Of Hilbert--Huang Transform And Support Vector Machine For Detection And Classification Of Voltage Sag Sources, Alireza Foroughi, Ebrahim Mohammadi, Saeid Esmaeili
Turkish Journal of Electrical Engineering and Computer Sciences
Power quality disturbances, including voltage sag, swell, harmonics, flicker, and notch, are one of the main concerns for industries and electrical equipment. Among these disturbances, voltage sag, due to its irrecoverable economic effects on industries, is particularly important. In this paper, the detection and classification of voltage sag sources containing motor starting, short circuit, transformer energizing, and the reacceleration of motors after fault clearance using the Hilbert--Huang transform (HHT) and support vector machine (SVM) are studied. A voltage sag waveform includes several oscillating modes; for separating these oscillating modes, which are called intrinsic mode functions (IMFs), empirical mode decomposition is …
A Computer-Aided Diagnosis System For Breast Cancer Detection By Using A Curvelet Transform, Nebi̇ Gedi̇k, Ayten Atasoy
A Computer-Aided Diagnosis System For Breast Cancer Detection By Using A Curvelet Transform, Nebi̇ Gedi̇k, Ayten Atasoy
Turkish Journal of Electrical Engineering and Computer Sciences
The most common type of cancer among women worldwide is breast cancer. Early detection of breast cancer is very important to reduce the fatality rate. For the hundreds of mammographic images scanned by a radiologist, only a few are cancerous. While detecting abnormalities, some of them may be missed, as the detection of suspicious and abnormal images is a recurrent mission that causes fatigue and eyestrain. In this paper, a computer-aided diagnosis system using the curvelet transform (CT) algorithm is proposed for interpreting mammograms to improve the decision making. The purpose of this study is to develop a method for …
Data Mining Of Protein Databases, Christopher Assi
Data Mining Of Protein Databases, Christopher Assi
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …
Skewed Alpha-Stable Distributions For Modeling And Classification Of Musical Instruments, Mehmet Erdal Özbek, Mehmet Emre Çek, Feri̇t Acar Savaci
Skewed Alpha-Stable Distributions For Modeling And Classification Of Musical Instruments, Mehmet Erdal Özbek, Mehmet Emre Çek, Feri̇t Acar Savaci
Turkish Journal of Electrical Engineering and Computer Sciences
Music information retrieval and particularly musical instrument classification has become a very popular research area for the last few decades. Although in the literature many feature sets have been proposed to represent the musical instrument sounds, there is still need to find a superior feature set to achieve better classification performance. In this paper, we propose to use the parameters of skewed alpha-stable distribution of sub-band wavelet coefficients of musical sounds as features and show the effectiveness of this new feature set for musical instrument classification. We compare the classification performance with the features constructed from the parameters of generalized …