Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 2Parkinson hastalığı sınıflandırmasına yönelik ivmeölçer tabanlı zamanlama analizi(IEEE, 2023) Karaçalı, Bilge; Onay, FatihParkinson's disease is a neurodegenerative disorder caused by dopamine deficiency in the basal ganglia, resulting in cognitive and motor impairments. In this study, accelerometer signals were used to estimate the delay time between the command to start pedaling and the actual movement onset in three groups: healthy individuals (n=13), Parkinson's disease patients (n=13), and patients with freezing of gait symptoms (n=13). Features were extracted from the delay time distributions for each participant and subjected to a triple classification. Linear support vector machine achieved a classification accuracy of 69.2% for all participants. Notably, the average time to start pedaling was found to be significantly different among the three groups, and accelerometer-based timing analysis could be used as a diagnostic tool to assist clinical tests.Conference Object Algıda gecikme ve kısa-ömürlü senkronizasyon temelli yeni bir hayali motor aktivite tanıma yaklaşımı(IEEE, 2023) Olcay, B. Orkan; Karaçalı, BilgeThis study proposes a novel approach for investigating a brain-computer interface that considers the temporal organization of brain activity, explicitly accounting for perception latency. To this end, we aligned the onset of task periods with the concurrence of left parietal and parieto-occipital electrodes to obtain the timings of perception latencies. Then, activity-specific synchronization timings between channel pairs were calculated using the time-aligned task periods. The perception latency and activity-specific synchronization timings were subsequently used for feature extraction and classification. The proposed approach achieved significantly better performance when comparing the proposed approach with the method that did not account for the perception latencyConference Object Citation - WoS: 2Türk Makam Müziği Notaları için Otomatik Ezgi Bölütleme(Institute of Electrical and Electronics Engineers Inc., 2014) Bozkurt, Barış; Karaçalı, Bilge; Karaosmanoğlu, M. Kemal; Ünal, ErdemAutomatic melodic segmentation is one of the important steps in computational analysis of melodic content from symbolic data This widely studied research problem has been very rarely considered for Turkish makam music. In this paper we first present test results for state-of-the-art techniques from literature on Turkish makam music data Then, we present a statistical classification-based segmentation system that exploits the link between makant melodies and usul and makam scale hierarchies together with the well-known features in literature. We show through tests on a large dataset that the proposed system has a higher accuracy.Conference Object Citation - Scopus: 2Model-Free Expectation Maximization for Divisive Hierarchical Clustering of Multicolor Flow Cytometry Data(IEEE, 2014) Köktürk, Başak Esin; Karaçalı, BilgeThis paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm. The divisions are carried out until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-color flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any knowledge on the number of clusters or their distribution models.Conference Object Citation - Scopus: 1Dijital Sitolojide Kanser Tanıma için Analitik ve Öngörüsel Yarı-güdümlü Öğrenme(IEEE, 2012) Karaçalı, BilgeIn this work, cancer recognition in digital cytology data was carried out using quasi-supervised learning. The data subject to recognition contained ground-truth data only in the form of a labeled set of cancer-free samples and the cancerous samples were provided along with cancer-free samples in an unlabeled mixed dataset. In this framework, a predictive method was derived to label future samples as cancerous or cancer-free based on this data at hand together with an analytical method to label the cancerous samples in the mixed dataset. In the experiments, the methods based on the quasi-supervised learning algorithm achieved higher recognition performance in both cases than the alternative approaches based on supervised support vector machine classifiers. These results indicate that the quasi-supervised learning is the only valid approach in both analytical and predictive recognition when only labeled cancer-free samples are available for statistical learning. © 2012 IEEE.Conference Object Citation - Scopus: 1Elektroensefalografi Verilerinin Yarı-güdümlü Öğrenme ile Otomatik Olarak İşaretlenmesi(IEEE, 2012) Köktürk, Başak Esin; Karaçalı, BilgeIn this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quasi-supervised learning. The data feature vectors were constructed using independent component analysis and wavelet transform, and then, these feature vectors were separated using quasi-supervised learning. Experiment results showed that the EEG data of the stimuli can be separated using quasi-supervised learning. © 2012 IEEE.Conference Object Citation - Scopus: 1Hierarchical Motif Vectors for Amino Acid Sequence Alignment(2012) Karaçalı, BilgeWe present a new framework for global and local alignment of amino acid sequences based on hierarchical motif vectors that characterize local amino acid configurations. The motif vectors are constructed by carrying out wavelet decomposition on numeric property sequences obtained by replacing each amino acid in a sequence with their respective properties, and concatenating such profiles obtained for a large number of physico-chemical properties into a single column vector. We then formulate different schemes for aligning amino acid sequences based on their respective motif vectors globally as well as locally subject to measures of statistical significance. Experiment results indicate that the motif vectors accurately capture the amino acid composition at and around individual sites along sequences and allow associating sequence segments sharing similar functional attributes.Conference Object Farklı Kontrastlı Medi̇kal Görüntülerde Elasti̇k Hi̇zalama İ̇çi̇n Ni̇rengi̇ Noktalarının Beli̇rlenmesi̇ ve Sınanması(IEEE, 2011) Karaçalı, BilgeIn this study, automatic identification of landmarks was carried out on real double-echo PD/T2 Magnetic Resonance images for use in multi-modality deformable image registration algorithms. To this end, the corner points of the PD-weighted Magnetic Resonance image selected as the reference image were identified at varying fields of view and used as landmark candidates after eliminating the repeated points and those at close proximity. Next, the matching performances of these candidates were determined by invoking a localization algorithm that optimizes information theoretic point similarity measures derived earlier after random initialization at varying distances and directions. In consideration to the best performing point similarity measures and neighborhood radii at landmarks providing successful localization, the mutual information-based point similarity measure evaluated over large neighborhoods was preferred at a conspicuously higher rate than the other alternatives. © 2011 IEEE.Conference Object Citation - Scopus: 3Gri Seviye Birliktelik Matrisi Öznitelikleri ve Manifold Öğrenme Yardımıyla Histoloji Görüntülerinde Otomatik Doku Sınıflandırılması(Institute of Electrical and Electronics Engineers Inc., 2009) Önder, Devrim; Karaçalı, BilgeThe aim of this work is to perform automated texture classification of histology slides using grayscale images and manifold learning method. Texture feature vectors were obtained using local gray scale co-occurrence matrices and the dimension of the feature vector space was lowered using Isomap dimension reduction. In a lower dimension feature space, k-means clustering operation was performed in order to provide separate texture clusters. In this work, experimental results were obtained using human kidney histology slides. Corresponding feature vectors and determined texture types were given as results.Conference Object Citation - Scopus: 2Histoloji Görüntülerinde Kanserli Desenlerin Yarı Güdümlü Öğrenme Yöntemiyle Tam Otomatik Sınıflandırılması(Institute of Electrical and Electronics Engineers, 2010) Önder, Devrim; Sarıoğlu, Sülen; Karaçalı, BilgeThe aim of this work is to perform automated texture classification of histology slide images in health and cancerous conditions using quasi-supervised statistical learning method. Tissue images were acquired from histological slides of human colon and were seperated into two groups in terms of normal and disease conditions. Texture feature vectors corresponding to tissue segments of each image were calculated using co-occurrence matrices. Different texture regions were determined by the quasi-supervised statistical learning method using texture features of normal and cancerous groups. ©2010 IEEE.