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 - WoS: 2Citation - Scopus: 4Çok-etiketli Film Türü Sınıflandırması için Türkçe Konu Modellemesi Veri Kümesi(Institute of Electrical and Electronics Engineers, 2020) Jabrayilzade, Elgün; Poyraz Arslan, Algın; Para, Hasan; Polatbilek, Ozan; Sezerer, Erhan; Tekir, SelmaStatistical topic modeling aims to assign topics to documents in an unsupervised way. Latent Dirichlet Allocation (LDA) is the standard model for topic modeling. It shows good performance on document collections, documents being relatively long texts but it has poor performance on short texts. Topic modeling on short texts is on the rise due to the potential of social media. Thus, approaches that are able to nd topics on short texts as well as long texts are sought. However, there is a lack of datasets that include both long and short texts which have the same ground-truth categories. In this work, we release a Turkish movie dataset which contain both short lm descriptions and long subscripts where lm genre can be considered as topic. Furthermore, we provide multi-label movie genre classication results using a Feed Forward Neural Network (FFNN) taking LDA document-topic or Doc2Vec dense representations. © 2020 IEEE.Conference Object Citation - Scopus: 2Performance Analysis of Lattice Reduction Aided Mimo Detectors(Institute of Electrical and Electronics Engineers, 2012) Kılıçaslan, Kağan; Altınkaya, Mustafa AzizLattice reduction is a powerful method used in detection and precoding of wireless multiple input-multiple output (MIMO) systems. The basic idea is to consider the channel transfer matrix as a basis for the transmitted symbols. The channel transfer matrix is reduced to a more orthogonal matrix using lattice reduction algorithms. This in turn, improves the performance of conventional MIMO receivers. In this study, it is shown that this performance improvement depends on the modulation order. © 2012 IEEE.Conference Object Citation - Scopus: 6Klasi̇k Türk Müzi̇ği̇ İ̇çin Otomati̇k Notaya Dökme Si̇stemi̇(Institute of Electrical and Electronics Engineers, 2011) Bozkurt, Barış; Gedik, Ali Cenk; Karaosmanoğlu, M. KemalThis study presents an automatic transcription system for Turkish music for the first time in literature. We first discuss the characteristics of Turkish music that are taken into consideration in the design of the system. Then, the following signal processing components of the system are described briefly in relation to each other and explaining their function in the system: f0 estimation, automatic tonic detection and makam recognition based on pitch distributions, frequency and duration quantization. © 2011 IEEE.Conference Object Citation - Scopus: 9Çoklu Antenli̇ Bili̇şsel Radyo Si̇stemleri̇nde Dönemli̇-duraǧan Özelli̇k Algılama Yöntemi̇ne Dayanan Spektrum Sezi̇mi̇(Institute of Electrical and Electronics Engineers, 2011) Üstok, Refik Fatih; Özbek, BernaCognitive radios aim to implement dynamic frequency usage which is proposed to increase the efficiency of the spectrum and can sense their environment, detect the idle bands and then allocate the secondary users into the detected idle bands without any interference to primary users' communications. Cyclostationary Feature Detection is one of the commonly used methods in the literature for spectrum sensing. This paper focuses on sensing the idle bands using cyclostationary feature detection method in Cognitive Radio based systems. The performance is improved by using the proposed multiple antenna technique and the simulation results are analyzed. © 2011 IEEE.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.