Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Video Scene Classification Using Spatıal Pyramid Based Features(2014) Sert, Mustafa; Ergun, Hilal; https://orcid.org/0000-0002-7056-4245; AAB-8673-2019Recognition of video scenes is a challenging problem due to the unconstrained structure of the video content. Here, we propose a spatial pyramid based method for the recognition of video scenes and explore the effect of parameter optimization to the recognition accuracy. In the experiments different sampling methods, dictionary sizes, kernel methods, and pyramid levels are examined. Support Vector Machine (SVM) is employed for classification due to the success in pattern recognition applications. Our experiments show that, the size of dictionary and proper pyramid levels in feature representation drastically enhance the recognition accuracy.Item Sleep Apnea Detection Using Blood Pressure Signal(2018) Aksahin, Mehmet Feyzi; Oltu, Burcu; Karaca, Busra KubraSleep apnea is a common respiratory disease. Apnea affects sleep quality, reduces people's life standards, and it can result in death at advanced stage. Therefore the ability to detect the apnea quickly and accurately is important for the treatment of this disease. Apnea is diagnosed by specialists however this is a long and exhausting process. Accordingly, a decision support system that automatically diagnoses apnea has been developed to facilitate this process and make it more objective. The developed decision support system in this study is based on patient's blood pressure signals instead of traditional Polysomnography (PSG) records, which requires various physiological signals measured from the patients. In the examined blood pressure signals, the change that results from each heart beat was determined and heart rate variability (HRV) was calculated based on these changes. At the same time, maximum and minimum amplitude values were found for each change period and amplitude variability vector was created. The features for each epoch were determined using the generated amplitude variability vector and HRV data. Presence of apnea in each epoch is classified with determined features and with the use of "Quadratic SVM" classifier. The Quadratic SVM classifier was trained with 87.5% accuracy and then the system is tested. As a result 75.4% sensitivity and 75% positive predictive values were obtained.Item Facial Action Unit Detection using Variable Decision Thresholds(2016) Aksoy, Nukhet; Sert, Mustafa; 0000-0002-7056-4245; D-3080-2015Detection of facial action units (AUs) is an important research field for recognizing emotional states in facial expressions. Here, we propose a novel, yet effective method, that utilizes variable decision thresholds at the prediction stage of a binary learning method for AU detection. The method performs a thresholding technique to find optimum values for each AU and make use of these thresholds as the decision threshold of the support vector machine (SVM) algorithm. Our experiments on Extended Cohn- Kanade (CK+) dataset show significant improvements on most of the AUs with an average F1 score of 6 .383 % compared with the baseline method.Item Development of a MFCC-SVM Based Turkish Speech Recognition System(2016) Tombaloglu, Burak; Erdem, HamitIn this study, a SVM-MFCC based Turkish Speech Recognition system is devoloped. In the structure, Mel Frequency Cepstral Coefficients (MFCC) are used for feature extraction and Support Vector Machines(SVM) are used for classification of the phonemes. Three more phoneme recognition methods are applied to same dataset and their perfomance is compared. The applied methods are the combination of the Linear Prediction Cepstral Coefficients (LPCC), which is a commonly used method of feature extraction and Hidden Markov Method (HMM) which is a known classification method. The applied feature extraction and classification methods has been selected due to phoneme-based property of the Turkish language.Item Continuous Wavelet Transform Based Method For Detection Of Arousal(2017) Kantar, Tugce; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019Sleeping is a vital biological requirement in the homeostatic mechanism. Sleep disorders can cause personal health problems and life quality deterioration. Transient waveforms (k-complexes, sleep spindles, arousal, etc.) happens instantaneously in sleep, have distinctive structural features, amplitudes and frequencies, and are difficult to distinguish from the background of electroencephalography (EEG) which are called the microstructure of the EEG. Microstructure analysis is important for brain research, sleep studies, sleep stage scorings and assessment of sleep disorders. Detection of arousal, one of these structures, is performed by visual scoring of all night sleep records by an expert sleep physician. In particular, visual analysis of the microstructure is difficult and time-consuming task so it increases error rates in scoring. The aim of this study is automatic detection of arousals from EEG signals. A computer-aided diagnosis system that can detect arousal can give more objective results to the expert sleep experts for diagnosing. In this study, continuous wavelet transform analysis of scored EEG signals was performed and features were obtained As a result, a decision support system algorithm for arousal has been developed using the obtained features and support vector machines. The specificity of the algorithm is 100% and sensitivity is 95.45%.Item Detection of K-complexes in Sleep EEG With Support Vector Machines(2017) Kantar, Tugce; Erdamar, Aykut; 0000-0001-8588-480X; AAA-6844-2019Sleep is a state that can be characterized by the electrical oscillations of nerve cells, where brain activity is more stable than waking. Transient waveforms observed in sleep electroencephalography are structures with specific amplitude and frequency characteristics that can occur in some stages of sleep. The determination of the k-complex, which is one of these structures, is performed by visual scoring of all night sleep recordings by expert physicians. For this reason, a decision support system that allows automatic detection of the k-complex can give physicians more objective results in diagnosis. In this study, sleep EEG records scored by a physician were analyzed in different methods from the literature. Three features have been determined that express the k-complex presence and k-complexes were detected using these features and support vector machines. As a result, the performance of the algorithm was evaluated and sensitivity and specificity were determined as 70.83% and 85.29%, respectively.