Browsing by Subject "Support vector machines"
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Item Open Access Activity recognition invariant to sensor orientation with wearable motion sensors(MDPI AG, 2017) Yurtman, A.; Barshan, B.Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.Item Open Access Akut koroner sendromların otomatik ST/T sınıflandırıcısı ile erken tanısı(IEEE, 2014-10) Terzi, M. Begüm; Arıkan, Orhan; Abacı, A.; Candemir, M.; Dedoğlu, MehmetIn patients with acute coronary syndrome, temporary chest pains together with changes in ECG ST segment and T wave occur shortly before the start of myocardial infarction. In order to diagnose acute coronary syndromes early, a new technique which detects changes in ECG ST/T sections is developed. As a result of implementing the developed technique to real ECG recordings, it is shown that the proposed technique provides reliable detections. Therefore, the developed technique is expected to provide early diagnosis of acute coronary syndromes which will lead to a significant decrease in heart failure and mortality rates. © 2014 IEEE.Item Open Access Akut koroner sendromun destek vektör makinelerine ve EKG’ye dayalı tespiti(IEEE, 2019-04) Terzi, Merve Begüm; Arıkan, OrhanAkut koroner sendroma (AKS) sahip hastalarda, miyokard infarktüsü başlangıcından önce geçici göğüs ağrıları ile birlikte EKG sinyalinin ST segmentinde ve T dalgasında değişiklikler meydana gelmektedir. Bu çalışmada, AKS’nin gürbüz tespitini gerçekleştirmek amacıyla, EKG sinyalinin ST segmentindeki ve T dalgasındaki anomalileri güncel sinyal işleme ve makine öğrenmesi tekniklerini kullanarak tespit eden bir teknik geliştirilmiştir. Bu amaçla, STAFF III veri tabanındaki geniş bantlı kayıtlar kullanılarak, AKS’nin teşhisi için ayırıcılığı en yüksek olan EKG özniteliklerini elde eden özgün bir öznitelik çıkarım tekniği geliştirilmiştir. Elde edilen kritik öznitelikleri kullanarak, AKS’nin gürbüz tespitini gerçekleştiren destek vektör makinelerine (DVM) ve çekirdek fonksiyonlarına dayalı bir gözetimli öğrenme tekniği geliştirilmiştir. Önerilen tekniğin STAFF III veri tabanındaki kayda değer sayıda hastadan elde edilen başarım sonuçları, tekniğin oldukça güvenilir AKS tespiti sağladığını göstermektedir.Item Open Access Authorship attribution: performance of various features and classification methods(IEEE, 2007-11) Bozkurt, İlker Nadi; Bağlıoğlu, Özgür; Uyar, ErkanAuthorship attribution is the process of determining the writer of a document. In literature, there are lots of classification techniques conducted in this process. In this paper we explore information retrieval methods such as tf-idf structure with support vector machines, parametric and nonparametric methods with supervised and unsupervised (clustering) classification techniques in authorship attribution. We performed various experiments with articles gathered from Turkish newspaper Milliyet. We performed experiments on different features extracted from these texts with different classifiers, and combined these results to improve our success rates. We identified which classifiers give satisfactory results on which feature sets. According to experiments, the success rates dramatically changes with different combinations, however the best among them are support vector classifier with bag of words, and Gaussian with function words. ©2007 IEEE.Item Open Access Automatic radar antenna scan type recognition in electronic warfare(Institute of Electrical and Electronics Engineers, 2011-10-04) Barshan, B.; Eravci, B.We propose a novel and robust algorithm for antenna scan type (AST) recognition in electronic warfare (EW). The stages of the algorithm are scan period estimation, preprocessing (normalization, resampling, averaging), feature extraction, and classification. Naive Bayes (NB), decision-tree (DT), artificial neural network (ANN), and support vector machine (SVM) classifiers are used to classify five different ASTs in simulation and real experiments. Classifiers are compared based on their accuracy, noise robustness, and computational complexity. DT classifiers are found to outperform the others.Item Open Access Autonomous multiple teams establishment for mobile sensor networks by SVMs within a potential field(2012) Nazlibilek, S.In this work, a new method and algorithm for autonomous teams establishment with mobile sensor network units by SVMs based on task allocations within a potential field is proposed. The sensor network deployed into the environment using the algorithm is composed of robot units with sensing capability of magnetic anomaly of the earth. A new algorithm is developed for task assignment. It is based on the optimization of weights between robots and tasks. The weights are composed of skill ratings of the robots and priorities of the tasks. Multiple teams of mobile units are established in a local area based on these mission vectors. A mission vector is the genetic and gained background information of the mobile units. The genetic background is the inherent structure of their knowledge base in a vector form but it can be dynamically updated with the information gained later on by experience. The mission is performed in a magnetic anomaly environment. The initial values of the mission vectors are loaded by the task assignment algorithm. The mission vectors are updated at the beginning of each sampling period of the motion. Then the teams of robots are created by the support vector machines. A linear optimal hyperplane is calculated by the use of SVM algorithm during training period. Then the robots are classified as teams by use of SVM mechanism embedded in the robots. The support vector machines are implemented in the robots by ordinary op-amps and basic logical gates. Team establishment is tested by simulations and a practical test-bed. Both simulations and the actual operation of the system prove that the system functions satisfactorily. © 2012 Elsevier Ltd. All rights reserved.Item Open Access Bilkent-RETINA at retrieving diverse social images task of MediaEval 2014(CEUR-WS, 2014-10) Saraç, Mustafa İlker; Duygulu PınarThis paper explains the approach proposed by Bilkent - RETINA team for the Retrieving Diverse Social Images task of MediaEval 2014 [1]. We develop a framework which rst removes outliers using one-class support vector machines (SVM) to improve relevance. Second it clusters the eliminated set and retrieves the centroids to diversify the results. We tried to exploit visual only features during our experiments. For the rst run we used the provided visual features and for the second run we used well known visual features like SIFT [2] and GIST [4].Item Open Access Canlı hücre bölütlemesi için gözeticili öğrenme modeli(IEEE Computer Society, 2014-04) Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, ÇiğdemAutomated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE.Item Open Access Carcinoma cell line discrimination in microscopic images using unbalanced wavelets(IEEE, 2012-03) Keskin, Furkan; Suhre, Alexander; Erşahin, Tüli,; Çetin Atalay, Rengül; Çetin, A. EnisCancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE.Item Open Access Cepstrum based feature extraction method for fungus detection(SPIE, 2011) Yorulmaz, Onur; Pearson, T.C.; Çetin, A. EnisIn this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).Item Open Access Classification of regional ionospheric disturbance based on machine learning techniques(European Space Agency, 2016) Terzi, Merve Begüm; Arıkan, Orhan; Karatay, S.; Arıkan, F.; Gulyaeva, T.In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.Item Open Access Classifying fonts and calligraphy styles using complex wavelet transform(Springer-Verlag London Ltd, 2015) Bozkurt, A.; Duygulu P.; Cetin, A.E.Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases. A generic font recognition system independent of language, script and content is desirable for processing various types of documents. At the same time, categorizing calligraphy styles in handwritten manuscripts is important for paleographic analysis, but has not been studied sufficiently in the literature. We address the font recognition problem as analysis and categorization of textures. We extract features using complex wavelet transform and use support vector machines for classification. Extensive experimental evaluations on different datasets in four languages and comparisons with state-of-the-art studies show that our proposed method achieves higher recognition accuracy while being computationally simpler. Furthermore, on a new dataset generated from Ottoman manuscripts, we show that the proposed method can also be used for categorizing Ottoman calligraphy with high accuracy. © 2015, Springer-Verlag London.Item Open Access Classifying human leg motions with uniaxial piezoelectric gyroscopes(2009) Tunçel O.; Altun, K.; Barshan, B.This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost. © 2009 by the authors.Item Open Access Comparative study on classifying human activities with miniature inertial and magnetic sensors(Elsevier, 2010) Altun, K.; Barshan, B.; Tunçel, O.This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.Item Open Access Covariance matrix-based fire and flame detection method in video(Springer, 2011-09-17) Habiboğlu, Y. H.; Günay, O.; Çetin, A. EnisThis paper proposes a video-based fire detection system which uses color, spatial and temporal information. The system divides the video into spatio-temporal blocks and uses covariance-based features extracted from these blocks to detect fire. Feature vectors take advantage of both the spatial and the temporal characteristics of flame-colored regions. The extracted features are trained and tested using a support vector machine (SVM) classifier. The system does not use a background subtraction method to segment moving regions and can be used, to some extent, with non-stationary cameras. The computationally efficient method can process 320×240 video frames at around 20 frames per second in an ordinary PC with a dual core 2.2 GHz processor. In addition, it is shown to outperform a previous method in terms of detection performance.Item Open Access Detection of acute myocardial ischemia based on support vector machines(IEEE, 2018) Terzi, Merve Begüm; Arıkan, OrhanIn patients with acute myocardial ischemia, chest pains together with changes in ST/T sections of ECG signal occur before the start of myocardial infarction. In this study, in order to diagnose acute myocardial ischemia, a technique which automatically detects changes in ST/T sections of ECG is developed. For this purpose, by using ECG recordings of STAFF III database, ECG features that are critical in the detection of acute myocardial ischemia are identified. By using support vector machines (SVM) operating with linear and radial basis function (RBF) kernels, classifiers that use two and four most discriminating features of ST/T sections of ECG signal are designed. As a result of implementing the developed technique on ECG recordings of STAFF III database, obtained results over a considerable number of patients indicate that the proposed technique provides highly reliable detection of acute myocardial ischemia. Therefore, by using the developed technique, early and accurate diagnosis of acute myocardial ischemia can be performed, which can lead to a significant decrease in morbidity and mortality rates.Item Open Access Detection of empty hazelnuts from fully developed nuts by impact acoustics(IEEE, 2005) Onaran, İbrahim; Dülek, Berkan; Pearson, T. C.; Yardımcı, Y.; Çetin, A. EnisShell-kernel weight ratio is the main determinate of quality and price of hazelnuts. Empty hazelnuts and nuts containing undeveloped kernels may also contain mycotoxin producing molds, which can cause cancer. A prototype system was set up to detect empty hazelnuts by dropping them onto a steel plate and processing the acoustic signal generated when kernels impact the plate. The acoustic signal was processed by five different methods: 1) modeling of the signal in the time domain, 2) computing time domain signal variances in short time windows, 3) analysis of the frequency spectra magnitudes, 4) maximum amplitude values in short time windows, and 5) line spectral frequencies (LSFs). Support Vector Machines (SVMs) were used to select a subset of features and perform classification. 98% of fully developed kernels and 97% of empty kernels were correctly classified.Item Open Access Detection of fungal damaged popcorn using image property covariance features(Elsevier, 2012) Yorulmaz, O.; Pearson, T. C.; Çetin, A.Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.Item Open Access Düşme tespiti için sınıflandırma yöntemlerinin karşılaştırılması(IEEE, 2014-04) Çatalbaş, Bahadır; Yücesoy, Burak; Seçer, G.; Aslan, MuratBu bildiride giyilebilir yapıda olan ve üç boyutlu ölçüm alabilen bir ivmeölçerin çıktılarını kullanarak düşme tespiti yapan farklı algoritmaların karşılaştırılması yapılmıştır. Karşılaştırma amacıyla destek vektör makineleri, yapay sinir ağları ile elde edilen sınıflandırıcılar ve kural bazlı bir sınıflandırıcı kullanılmıştır. Sınıflandırıcıların tasarlanması ve dogrulanması amacıyla 7 farklı denekten üçer defa düşme ve düşme dışındaki günlük aktivitelere ilişkin ivmeölçer verileri toplanmıştır. Yapılan karşılaştırma sonucunda tespit doğruluğu en yüksek algoritmanın %87,76 ile destek vektör makineleri olduğu bulunmuştur. En yüksek düşme tespit oranı da %90,91 ˘ olarak kural bazlı sınıflandırıcı kullanımıyla elde edilmiştir. En yüksek özgüllük oranı %89,47 ile yine destek vektör makineleri ile elde edilmiştir.Item Open Access Fall detection using single-tree complex wavelet transform(Elsevier, 2013) Yazar, A.; Keskin, F.; Töreyin, B. U.; Çetin, A. EnisThe goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.
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