Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
Browse
24 results
Search Results
Publication Metadata only Augmenting clinical observations with visual features from longitudinal MRI data for improved dementia diagnosis(2010) Ünay, Devrim; Ünay, Devrim, Bahçeşehir Üniversitesi, Istanbul, TurkeyImage-based diagnosis in the medical area often requires qualitative interpretation from the experts, despite the high-resolution of the acquired images. Computation of quantitative measures and comparison of multiple patients using automated medical image analysis tools will help improve the diagnosis and efficiency, especially in the areas such as neurology, where diagnosis from one patient's data has limitations and the prevalence of neurodegenerative diseases is expected to substantially increase in the near future due to the aging population. To this end, this paper presents a novel work on fusing clinical and patient-demographics related observations with visual features computed from brain longitudinal MRI (magnetic resonance imaging) data for improved dementia diagnosis. Experiments with real data showed that augmenting cognitive scores with visual features from a subset of subcortical structures results in more accurate diagnosis. Moreover, subset of structures typically selected are consistent with those (being) investigated in the literature. Copyright 2010 ACM. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Determinig the ligand-specific regions of peptide-binding G-Protein Coupled Receptors(2010) Çobanoǧlu, Murat Can; Sezerman, Osman Uğur; Karabulut, Nermin Pinar; Çobanoǧlu, Murat Can, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Sezerman, Osman Uğur, Faculty of Engineering and Natural Sciences, Sabancı Üniversitesi, Tuzla, Turkey; Karabulut, Nermin Pinar, Department of Computer Science, Bahçeşehir Üniversitesi, Istanbul, TurkeyThe G-Protein Coupled Receptor (GPCR) proteins are of paramount importance for pharmaceutical research as this single family of proteins is targeted by more than 50% of all modern drugs. Their ligands include but are not limited to small molecules, peptides and photons. Consequently it is vital to know the sites of interaction between the ligand and the receptor. These sites can be used for a multitude of purposes such as classification of sequences or drug design. We use the feature extraction method proposed in [1] to analyze the ligand-specific regions among various Peptide-binding GPCRs. We present the features that will help drug design researchers enhance specificity of peptide-binding drugs. © 2009 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Enhanced feature selection from wavelet packet coefficients in fault diagnosis of induction motors with artificial neural networks(2010) Eren, Levent; Cekic, Yalcin; Devaney, Michael Joseph; Eren, Levent, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Cekic, Yalcin, College of Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Devaney, Michael Joseph, College of Engineering, Columbia, United StatesWavelet packet decomposition (WPD) of line current has been successfully applied in motor fault detection. Enhanced feature selection from wavelet packet coefficients (WPCs) is presented in this paper. This method involves the decomposition of motor current into equally spaced frequency bands by using an all-pass implementation of elliptic IIR half-band filters in the filter bank structure to obtain WPCs in a computationally efficient way. Then, the bias in WPCs for each frequency band is removed to suppress both power system harmonics and leakage from adjacent frequency bands. Finally, the enhanced features are used as inputs to an ANN to provide motor fault detection with higher fault detection rate. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only A hybrid method for feature selection based on mutual information and canonical correlation analysis(2010) Sakar, C. Okan; Kursun, Olcay; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, TurkeyMutual Information (MI) is a classical and widely used dependence measure that generally can serve as a good feature selection algorithm. However, under-sampled classes or rare but certain relations are overlooked by this measure, which can result in missing relevant features that could be very predictive of variables of interest, such as certain phenotypes or disorders in biomedical research, rare but dangerous factors in ecology, intrusions in network systems, etc. On the other hand, Kernel Canonical Correlation Analysis (KCCA) is a nonlinear correlation measure effectively used to detect independence but its use for feature selection or ranking is limited due to the fact that its formulation is not intended to measure the amount of information (entropy) of the dependence. In this paper, we propose Predictive Mutual Information (PMI), a hybrid measure of relevance not only is based on MI but also accounts for predictability of signals from one another as in KCCA. We show that PMI has more improved feature detection capability than MI and KCCA, especially in catching suspicious coincidences that are rare but potentially important not only for subsequent experimental studies but also for building computational predictive models which is demonstrated on two toy datasets and a real intrusion detection system dataset. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only Prediction of protein sub-nuclear location by clustering mRMR ensemble feature selection(2010) Sakar, C. Okan; Kursun, Olcay; Şeker, Hüseyin; Gürgen, Fïkret S.; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Department of Computer Engineering, Istanbul Üniversitesi, Istanbul, Turkey; Şeker, Hüseyin, Department of Informatics, De Montfort University, Leicester, United Kingdom; Gürgen, Fïkret S., Department of Computer Engineering, Boğaziçi Üniversitesi, Bebek, TurkeyIn many applications of pattern recognition in the bioinformatics and biomedical fields, input variables are organized into natural partitions that are called views in the literature. Mutual information can be used in selecting a minimal yet capable subset of views. Ignoring the presence of views, dismantling them, and treating their variables intermixed along with those of others at best results in a complex uninterpretable predictive system for researchers in these fields. Moreover, it would require measuring or computing majority of the views. We use the clustering indices of the views and rank the views according to the unique information they have with the target using minimum redundancy-maximum relevance (mRMR) approach. We also propose an ensemble approach to reduce the random variations in clusterings. © 2010 IEEE. © 2010 Elsevier B.V., All rights reserved.Publication Metadata only INTERSPEECH 2009 emotion recognition challenge evaluation, INTERSPEECH 2009 duygu tanima yarişmasi deǧerlendirmesi(2010) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, TurkeyIn this paper we evaluate INTERSPEECH 2009 Emotion Recognition Challenge results. The challenge presents the problem of accurate classification of natural and emotionally rich FAU Aibo recordings into five and two emotion classes. We evaluate prosody related, spectral and HMM-based features with Gaussian mixture model (GMM) classifiers to attack this problem. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of pitch, first derivative of pitch and intensity. We employ unsupervised training of HMM structures with prosody related temporal features to define HMM-based features. We also investigate data fusion of different features and decision fusion of different classifiers to improve emotion recognition results. Our two-stage decision fusion method achieves 41.59 % and 67.90 % recall rate for the five and two-class problems, respectively and takes second and fourth place among the overall challenge results. ©2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only A comparison of geometrical facial features for affect recognition, Duygu tanima i̇çi̇n geometri̇k yüz özni̇teli̇kleri̇ni̇n karşilaştirilmasi(2011) Ulukaya, Sezer; Erdem, Cigdem Eroglu; Ulukaya, Sezer, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this work, we compare two different geometric feature extraction methods derived from coordinates of facial points tracked by Active Appearance Models. The compared feature extraction methods differ in their use of coordinates or distances between facial points and whether they use the information of a neutral facial expression. Experiments on the extended Cohn-Kanade database show that the coordinate-based features using the neutral frame information gives the best emotion recognition results (%94) using a SVC classifier with a polynomial kernel. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech(2011) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, College of Engineering, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, College of Engineering, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Department of Electrical and Electronic Engineering, Özyeğin Üniversitesi, Istanbul, TurkeyTraining datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequency (LSF) features indicate that utilization of RANSAC in the training phase provides an improvement in the unweighted recall rates on the test set. Experimental studies performed over the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF and MFCC based classifiers provide further significant performance improvements. © 2011 Springer-Verlag. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Feature extraction using time-frequency/scale analysis and ensemble of feature sets for crackle detection(2011) Serbes, Görkem; Sakar, C. Okan; Kahya, Yasemin Palanduz; Aydın, Nizamettin; Serbes, Görkem, Department of Mechanical Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kahya, Yasemin Palanduz, Department of Electrical Engineering, Boğaziçi Üniversitesi, Bebek, Turkey; Aydın, Nizamettin, Department of Computer Engineering, Yıldız Teknik Üniversitesi, Istanbul, TurkeyPulmonary crackles are used as indicators for the diagnosis of different pulmonary disorders. Crackles are very common adventitious sounds which have transient characteristic. From the characteristics of crackles such as timing and number of occurrences, the type and the severity of the pulmonary diseases can be obtained. In this study, a novel method is proposed for crackle detection. In this method, various feature sets are extracted using time-frequency and time-scale analysis. The extracted feature sets are fed into support vector machines both individually and as an ensemble of networks. Besides, as a preprocessing stage in order to improve the success of the model, frequency bands containing no-information are removed using dual tree complex wavelet transform, which is a shift invariant transform with limited redundancy and an improved version of discrete wavelet transform. The comparative results of individual feature sets and ensemble of sets with pre-processed and non pre-processed data are proposed. © 2011 IEEE. © 2012 Elsevier B.V., All rights reserved.Publication Metadata only Feature extraction for facial expression recognition by canonical correlation analysis, Kanoni̇k korelasyon anali̇zi̇ i̇le yüz i̇fadesi̇nden duygu tanima i̇çi̇n özni̇teli̇ k çikarimi(2012) Sakar, C. Okan; Kursun, Olcay; Karaali, Ali; Erdem, Cigdem Eroglu; Sakar, C. Okan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Karaali, Ali, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyAlthough several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.
- «
- 1 (current)
- 2
- 3
- »