Browsing by Subject "Kernel functions"
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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 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 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 Identification of cancer patient subgroups via pathway based multi-view graph kernel clustering(2017-07) Ünal, Ali BurakCharacterizing patient genomic alterations through next-generation sequencing technologies opens up new opportunities for re ning cancer subtypes. Di erent omics data provide di erent views into the molecular biology of the tumors. However, tumor cells exhibit high levels of heterogeneity, and di erent patients harbor di erent combinations of molecular alterations. On the other hand, different alterations may perturb the same biological pathways. In this work, we propose a novel clustering procedure that quanti es the similarities of patients from their alteration pro les on pathways via a novel graph kernel. For each pathway and patient pair, a vertex labeled undirected graph is constructed based on the patient molecular alterations and the pathway interactions. The proposed smoothed shortest path graph kernel (smSPK) assesses similarities of pair of patients with respect to a pathway by comparing their vertex labeled graphs. Our clustering procedure involves two steps. In the rst step, the smSPK kernel matrices for each pathway and data type are computed for patient pairs to construct multiple kernel matrices and in the ensuing step, these kernel matrices are input to a multi-view kernel clustering algorithm to stratify patients. We apply our methodology to 361 renal cell carcinoma patients, using somatic mutations, gene and protein expressions data. This approach yields subgroup of patients that di er signi cantly in their survival times (p-value 1:5 108). The proposed methodology allows integrating other type of omics data and provides insight into disrupted pathways in each patient subgroup.Item Open Access Koroner arter hastalığının destek vektör makineleri ve Gauss karışım modeli ile tespiti(IEEE, 2019-04) Terzi, Merve Begüm; Arıkan, OrhanBu çalışmada, koroner arter hastalığının (KAH) gürbüz tespitini gerçekleştirmek amacıyla EKG’deki anomalileri güncel sinyal işleme ve makine ögrenmesi yöntemlerini kullanarak tespit eden bir teknik geliştirilmiştir. Bu amaçla, European ST-T veri tabanındaki geniş bantlı kayıtlar kullanılarak, KAH’ın güvenilir tespiti için kritik olan EKG özniteliklerini elde eden özgün bir öznitelik çıkarım tekniği geliştirilmiştir. Elde edilen öznitelikleri kullanarak, KAH’ın gürbüz tespitini gerçekleştiren destek vektör makinelerine (DVM) ve çekirdek fonksiyonlarına dayalı bir gözetimli öğrenme tekniği geliştirilmiştir. İskemik EKG verilerinin eksik olduğu durumlarda, sadece bazal EKG verilerini kullanarak KAH’ın gürbüz tespitini gerçekleştiren Gauss karışım modeline (GKM) dayalı bir gözetimsiz ögrenme tekniği geliştirilmiştir. KAH’ı temsil eden aykırı değerlerin gürbüz tespitini gerçekleştirmek için Neyman-Pearson tipi bir yaklaşım geliştirilmiştir. Önerilen tekniğin European ST-T veri tabanı üzerindeki başarım sonuçları, tekniğin oldukça güvenilir KAH tespiti sağladığını göstermektedir.Item Open Access PDE control of a rotating shear beam with boundary feedback(IEEE, 2009-08) Doğan, M.; Morgül, ÖmerWe consider a flexible structure modeled as a shear beam which is clamped to a rigid body at one end and is free at the other end. The whole structure is free to rotate on the horizontal plane. We first model the system by using Partial Differential Equations (PDE) and we propose boundary feedback laws to achieve set point regulation of the rotation angle as well as to suppress the elastic vibrations. The proposed control laws are based on PDE model, hence we do not resort to discretization of the system equations by available methods. We utilize a coordinate transformation based on an invertible integral transformation by using Volterra form and backstepping techniques. We also present some simulation results. © 2009 EUCA.