WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/5

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3D dendritic spine segmentation using nonparametric shape priors
(Institute of Electrical and Electronics Engineers Inc., 2017) Bocugoz E.; Erdil E.; Argunsah A.O.; Unay D.; Cetin M.
Analyzing morphological and structural changes of dendritic spines in 2-photon microscopy images in time is important for neuroscience researchers. Correct segmentation of dendritic spines is an important step of developing robust and reliable automatic tools for such analysis. In this paper, we propose an approach for segmentation of 3D dendritic spines using nonparametric shape priors. The proposed method learns the prior distribution of shapes through Parzen density estimation on the training set of shapes. Then, the posterior distribution of shapes is obtained by combining the learned prior distribution with a data term in a Bayesian framework. Finally, the segmentation result that maximizes the posterior is found using active contours. Experimental results demonstrate that using nonparametric shape priors leads to better 3D dendritic spine segmentation results. © 2017 IEEE.
Accurate Dictionary Matching for Mr Fingerprinting Using Neural Networks and Feature Extraction
(Institute of Electrical and Electronics Engineers Inc., 2020) Soyak R.; Ersoy E.O.; Navruz E.; Fakultesi M.; Unay D.; Oksuz I.; Ersoy, Eda Ozgu; Unay, Devrim; Navruz, Ebru; Fakultesi, Muhendislik; Soyak, Refik; Oksuz, Ilkay
Magnetic Resonance Fingerprinting is a recent technique which aims at providing simultaneous measurements of multiple parameters. MRF works by varying acquisition parameters in a pseudorandom manner so as to get unique, uncorrelated signal evolutions from each tissue. MRF is a dictionary based approach, and thus requires a database. This database can be created by simulating the signal evolutions from first principles using different physical models for a wide variety of tissue parameter combinations. Having this dictionary, a pattern recognition algorithm is used to match the acquired signal evolutions from each voxel with each signal evolution in the dictionary. In this paper, we compare the efficiency of deep learning based feature extraction method and neural network architectures in order to achieve state-of-the-art accuracy in dictionary matching for MRF. Our results showcase successful dictionary matching with high accuracy both quantitatively and qualitatively. © 2020 IEEE.
Citation - WoS: 1
Adaptive Video Streaming With H.264 Sp Frames
(Pamukkale Univ, 2012) Sayit, Muge; Tunali, E. Thrhan
In this work, SP frames of the extended profile of H.264 AVC video codec are utilized to obtain efficient switching for actual video streaming experiments on the Internet. An adaptive algorithm is developed and by using this algorithm, performance of SP frames is compared with that of standard I-frame switching. Detailed measurements of network conditions are given for each experiment to indicate that comparisons are carried out in fair conditions. It has been observed that, under certain conditions, SP frames can improve performance considerably whereas in the others, they may only introduce overhead. Based on this observation different GOP patterns are proposed for different video type-congestion level combinations.
Advertising Adaptations Between Globalization and Locality: Case of Snickers
(Ankara Univ, Fac Communication, 2015) Kaptan, Yesim
By comparing the American and the Turkish versions of television commercials arising from the Snickers' global advertising campaign You are not you when you are hungry, this article examines how commercials are adapted to appeal to a sense of locality through the strategies of locality. To explore how locality is represented in advertising, three localization strategies (national language, culture, humor) employed in advertising adaptation are analyzed. Using interviews with focus groups and an advertising practitioner, the author argues that advertising plays an important role for construction and reproduction of locality in everyday life. The author asks which values and codes are employed in advertising adaptations and how they are articulated to discourse of consumer culture in the media materials. The article also discusses the significance of advertising adaptations within the overlapping contexts of global formats, national media, and local identities.
An Analysis Into the Persona of Knidia
(Turk Tarih Kurumu, 2009) Durna, Gül E.
[Abstract Not Available]
Citation - Scopus: 1
An Analysis of Change Over Time: Latent Growth Models
(Turkish Psychologists Assoc, 2010) Dural, Seda; Somer, Oya; Korkmaz, Mediha; Can, Seda; Ogretmen, Tuncay
Latent Growth Models which are used in understanding how individuals change over time have been a topic of intense interest among the researchers during the past two decades. These models in the framework of Structural Equation Modeling have been recommended as an alternative to classical methods such as analysis of variance. In this study, Latent Growth Models were introduced by using a Monte Carlo simulation approach and the interpretation of the findings was discussed. In addition, the effect of different sample sizes (30, 50, 100, and 200) on power and parameter estimates were examined. For this purpose; (1) data generation was performed with Monte Carlo simulation, (2) the parameters of unconditional and conditional models were estimated and the findings were discussed and (3) the effect of sample size on parameter estimates, standard errors, coverage and power was studied. All of the analyses were performed by using Mplus 5.1 software. Results were discussed in the context of advantages and disadvantages of Latent Growth Models, and the effect of sample size.
Citation - WoS: 2
Citation - Scopus: 2
Analysis of Nucleotide Changes in Rt-Pcr Primer/Probe Binding Regions in Sars-Cov Isolates Reported From Turkey
(Ankara Microbiology Soc, 2021) Demir, Ayse Banu; Bulgurcu, Alihan; Appak, Ozgur; Sayiner, Ayca Arzu
The SARS-CoV-2 virus, which caused the COVID-19 epidemic, caused more than 55 million cases and nearly 1.5 million deaths worldwide. For the microbiological diagnosis of the disease, the most valid method is detecting the presence of the viral genome by real-time reverse transcription polymerase chain reaction (rRT-PCR). However, due to the nature of the RNA viruses, frequent mutations may affect the sensitivity of the analyses made on the genetic material of the virus, such as PCR. In this study, we aimed to investigate the mutations in the primer-probe binding regions of the rRT-PCR panels used in COVID-19 diagnosis. SARS-CoV-2 whole genome sequence data (n= 194) isolated from COVID-19 cases in Turkey and uploaded on GISAID database from the centers in Istanbul (n= 78), Ankara (n= 58), Kars (n= 47), Bursa (n= 2), Adiyaman (n= 2), Erciyes (n= 1) and Kocaeli (n= 1) between March 17-September 14, 2020 were analyzed. In order to determine the nucleotide changes, SARS-CoV-2 sequences from Turkey were compared to the reference genome sequence (NC_045512.1) present in GenBank website. The constructed data set was aligned using the MAFFT program and was checked manually if the sequences were in the same frame by using the AliView program. Primer-probe binding sites of the thirteen SARS-CoV-2 rRT-PCR panels from seven different institutes (US CDC, China CDC, Charite CDC, Pasteur, HKU, Thailand, NIID) that are being used in COVID-19 diagnosis were evaluated in terms of nucleotide changes within the corresponding regions compared to the reference genome. Sequence diversities in the viral genomes were determined via positional nucleotide numerical calculator and entropy calculator modules and nucleotide and entropy changes in primer-probe binding regions for each rRT-PCR panel were examined. Among thirteen different primer-probe panels, nucleotide changes in the target regions of the seven primer-probe panels were determined. When viral sequences with nucleotide changes in the primer-probe binding regions were examined, the most common changes were observed in the China CDC N-forward primer and US CDC N3-forward primer binding regions. It is important that the kits to be used as diagnostic tests are designed specific to the regions with less nucleotide changes. Nucleotide changes may not be critical for DNA amplification for most PCR panels, but should be carefully monitored as they may affect the sensitivity of the assay. If the risk of alteration of the designed region is high, the primer - probe binding sites should be checked frequently and updated when necessary.
An Anthropomorphistic Approach To Measuring Civil Society Organization's Reputation: a Semantic Network Analysis
(Marmara Univ, Fac Communication, 2018) Turkel, Selin
The aim of the study is to reveal common meaning of reputation and disreputableness for civil society organizations (CSOs) by utilizing anthropomorphism. Accordingly, 212 individuals living in Izmir were asked to personify the CSOs they deemed reputable and disreputable, and provide adjectives that best described CSOs. In this study which was designed as descriptive research, semantic network analysis was employed and reputable and disreputable networks were visualized using a social network analysis software named Pajek. The relational analysis was performed using the measures of m-core, clique, articulation point, in-out degree, in-out closeness and betweenness. The first study to uncover the shared meaning of the CSO's reputation adopting the anthropomorphism approach reveals both positive and negative personality traits such as being helpful, honest, reliable and self-seeking, trickster, liar, and untrustworthy. The results of the research can contribute to the development of an appropriate measure of CSO's reputation for Turkish society.
Citation - Scopus: 1
Application of Evolutionary Algorithms To Garment Design
(2013) İnce, Türker; Vuruşkan A.; Bulgun E.; Güzeliş C.
In this study, we present the development of an intelligent system solution for fashion style selection for various female body shapes. The proposed intelligent system combines binary genetic algorithm (GA) or binary version of the particle swarm optimization (PSO) with PSO-trained artificial neural network. The former is used to search the solution space for the optimal design parameters corresponding to a best fit for the desired target, and the task of the latter is to evaluate fitness (goodness) of each evolved new fashion style. With the goal of creating natural aesthetic relationship between the shape of the body and the shape of the garment for fashion styling, combinations of upper body related and lower body related garment pieces together with detailed attribute categories were created as a knowledge base. The encouraging results of preliminary experiments demonstrate the feasibility of applying intelligent systems to fashion styling. © 2013 IEEE.
Citation - Scopus: 1
Attribute Value-Range Detection in Identification of Paraphrase Sentence Pairs
(Institute of Electrical and Electronics Engineers Inc., 2016) Kumova S.; Karaoglan B.; Kisla T.
Identification of paraphrase sentence pairs becomes increasingly prominent in natural language processing area (e.g plagiarism detection, summarization, machine translation). In this study, it is proposed to employ information gain measure in determining the value-ranges of the paraphrase classification features on the renown paraphrase corpus of Microsoft Research (MSRP). The classification performances of value-ranges that are determined by information gain measure and an alternative heuristic method are compared by the use of Bayes classifier. The results show that the proposed method performs better than the heuristic method. © 2016 IEEE.
Audio Melody Extraction From Heterophonic Turkish Maqam Music
(IEEE, 2021) Simsek, Berrak Ozturk; Akan, Aydin; Ozturk Simsek, Berrak
In this study, the Improved Variable Mode Decomposition Method (IVMD) is proposed for the estimation of the audio melody in heterophonic works that constitute the general texture of Turkish maqam music. In our study, the fundamental frequencies of the records belonging to huzzam, kurdilihicazkar, ussak, and rast maqams were estimated by using the IVMD method. Since the basis of the heterophonic texture is that the same melody is performed by more than one instrument, the estimated fundamental frequencies are more than one for each time window. After the multiple frequency estimation, in order to obtain the audio melody of the music recording and therefore a single frequency line, the selection of the frequencies belonging to the audio melody line from the fundamental frequencies was made. The study has been compared with the methods widely used in the analysis of polyphonic music works such as YIN and MELODIA. When the comparisons were evaluated on the basis of maqam and mixture according to the MIREX criteria, successful results were obtained with the IVMD method.
Audio Melody Extraction From Monophonic Turkish Maqam Music
(IEEE, 2020) Simsek, Berrak Ozturk; Akan, Aydin
In this study, a new method is proposed for predominant audio melody extraction in monophonic Turkish maqam music works. The music signals are decomposed using the Improved Variable Mode Decomposition Method and the fundamental frequencies are obtained by calculation of center frequencies on each mode. In order to estimate and selection of predominant melody line some parameters are determined from the different number of frequency information in each window. The obtained results are compared to YIN and MELODIA which are the signal processing algorithms used for western music. MIREX criterias are used in the evaluation step, adhering to international standards. Simulation results have shown that Improved Variable Mode Decomposition Method gives more successful results than other methods used in comparison.
Citation - WoS: 1
Citation - Scopus: 6
Automated Segmentation of Cells in Phase Contrast Optical Microscopy Time Series Images
(IEEE, 2019) Binici, Rifki Can; Sahin, Umut; Ayanzadeh, Aydin; Toreyin, Behcet Ugur; Onal, Sevgi; Okvur, Devrim Pesen; Ozuysal, Ozden Yalcin; Unay, Devrim
Phase contrast optical microscopy is a preferred imaging technique for live-cell, temporal analysis. Segmentation of cells from time series data acquired with this technique is a labor-intensive and time-consuming task that cell biology researchers need solution for. In this study traditional image processing and deep learning based approaches for automated cell segmentation from phase contrast optical microscopy time series are presented, and their performances are evaluated against manually annotated datasets.
Belediye Otobüslerinin Yht İstasyonu İçin İlçe Güzergâhlarının Çkkv İle Belirlenmesi: Kırıkkale İli Örneği
(2023) Bayram, Buse; Kara, Mert; Yumuşak, Rabia; Cürebal, Ahmet; Eren, Tamer
Günümüzde artan nüfusla birlikte bireysel araç sayısı ve dolayısıyla trafik yoğunluğu da her geçen gün artmaktadır. Hem şehir içi hem de şehirlerarası trafik yoğunluğunu azaltmaya yönelik olarak çalışmalar yapılmakta olup, şehirleri daha hızlı ve ekonomik bir şekilde birbirlerine bağlayan yüksek hızlı tren (YHT) projesi bunlardan biridir. Kırıkkale ilinde YHT istasyonunun kurulacak olması, efektif ilçe bağlantıları yapılarak daha fazla insanın kolay bir şekilde YHT hizmetini kullanması trafik yoğunluğu sorununa büyük etki edecektir. Bu bağlamda belediye otobüslerinin YHT istasyonu için ilçe bağlantılarının sağlanmasında Çok Kriterli Karar Verme (ÇKKV) yöntemlerinden AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order Preference by Similarity to An Ideal Solution) ve PROMETHEE (The Preference Ranking Organization Method for Enrichment Evaluation) entegre şekilde kullanılmıştır. Yöntem sonuçları karşılaştırıldığında Kırıkkale Üniversitesi – Osmangazi hattı için 2. güzergâhın seçildiği görülmekte olup, diğer ilçeler için de alternatif güzergâhların sıralamaları elde edilmiştir. Çalışma, Kırıkkale’de açılacak YHT durağı ile ilçelerin ve binlerce öğrencisi bulunan üniversitenin bağlantısını sağlamakta olduğundan, il bazında yapılmış en kapsamlı çalışma özelliğini taşımasının yanı sıra YHT için güzergâh belirleme çalışması olarak da literatüre önemli bir katkı sağlamaktadır.
Citation - WoS: 2
Between the State and the World Market: Small-Scale Hazelnut Production in the Black Sea Region
(Istanbul Univ, Fac Letters, Dept Sociology, 2020) Erköse, Hüseyin Yener; Sahin, Osman; Yukseker, Deniz; Sert, Deniz H.
Turkey is the world's largest hazelnut producer and exporter, yet hazelnut farmers have been growing hazelnuts in increasingly difficult conditions even for the years when production levels and hazelnut prices are high. In this paper, we take up the contradictions in hazelnut cultivation in Turkey and seek to show that, despite the commonsense opinion that the problem stems from small-scale cultivation, the more important problem is the unequal power relations that exist in the hazelnut market. We make the following arguments in the paper based on some of the findings from the field study we carried out in the Western and Eastern Black Sea regions in 2017. Issues exist regarding productivity and profitability in hazelnut cultivation characterized by small holdings. Hazelnut farmers are often unable to meet the expenditures and investments required for raising productivity. These problems arise more from the farmers' demographic profiles and debt levels and the unequal power relations in the hazelnut market with respect to small-scale production. Therefore, resolving the problems in hazelnut cultivation might require making changes that favor small farmers' power relations in the hazelnut market rather than enlarging holdings.
Citation - Scopus: 1
Binocular Vision Based Convolutional Networks
(Institute of Electrical and Electronics Engineers Inc., 2020) Oktar Y.; Ulucan O.; Karakaya D.; Ersoy E.O.; Türkan, Mehmet; Ulucan, Oguzhan; Ersoy, Eda Ozgu; Karakaya, Diclehan; Oktar, Yigit
It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investigation for improvements in such CNN-based visual imagery analysis applications. It is indeed questionable that if respective visual fields of each eye and the associated brain lobes are responsible for different learning abilities of the same scene. There are such open questions in this field of research which need rigorous investigation in order to further understand the nature of the human visual system, hence improve the currently available deep learning applications. This paper analyses a binocular CNNs architecture that is more analogous to the biological structure of the human visual system than the conventional deep learning techniques. While taking a structure called optic chiasma into account, this architecture consists of basically two parallel CNN structures associated with each visual field and the brain lobe, fully connected later possibly as in the primary visual cortex. Experimental results demonstrate that binocular learning of two different visual fields leads to better classification rates on average, when compared to classical CNN architectures. © 2020 IEEE.
Citation - WoS: 1
Citation - Scopus: 15
Blockchain Applications in Healthcare
(Institute of Electrical and Electronics Engineers Inc., 2018) Ekin A.; Unay D.
In this paper, we present the applications of blockchain technology in healthcare. Furthermore, we evaluate the choice and deployment of Blockchain technology in such applications, review the advantages and disadvantages of such an approach. We review the Estonian system, which is the first blockchain-based health system at the national level, in detail and discuss its ramifications to Turkey. This paper is one of the first papers in this domain and, to the best of authors' knowledge, the first in Turkish. © 2018 IEEE.
Brand Avoidance Behavior in Virtual Communities
(Bilgesel Yayincilik San & Tic Ltd, 2011) Demirbağ Kaplan, Melike; Ati̇k, Deniz; Gurkaynak, Nilgun
Brand avoidance behavior in virtual communities This study sought to understand the presence and role of virtual communities in brand avoidance behavior To this aim, consumer quotations regarding specific brands in Eksi Sozluk, which is one of the leading social media platforms in Turkey, were analyzed using a qualitative approach. The paper investigates the extent to which these perspectives can be classified in accordance with brand avoidance motives stated in present literature and aims to see if new dynamics exist. The study offers a new categorization for the reasons behind brand avoidanceIn addition, it contributes to the literature by highlighting a new dimension which emphasizes that repulsive marketing communication strategies could also lead to such avoidance.
Citation - WoS: 3
The Care of Patient With Urostomy
(Galenos Yayincilik, 2015) Harputlu, DENİZ
Urostomy surgery changes not only body structure but also lifestyle of the patients. Supporting patients both spiritual and mental as well as the development of their dexterity for the stoma care is important in the adaptation of their new life. During this process, stoma and wound care nurses have very special place.
Classification of Dementia Eeg Based on Sub-Bands Using Time-Frequency Approaches
(Institute of Electrical and Electronics Engineers Inc., 2022) Cura O.K.; Yilmaz G.C.; Ture H.S.; Akan A.; Yilmaz, Gulce Cosku; Ture, Hatice Sabiha; Cura, Ozlem Karabiber; Akan, Aydin
Alzheimer's dementia is a highly prevalent disorder among all neurological disorders. In this study, a new method based on time-Frequency (TF) representations such as Short Time Fourier Transform (STFT) and Synchrosqueezing Transform (SST) is proposed to classify EEG segments of AD patients and control subjects. Previous studies have shown that there are distinctive differences in the EEG signals of control subjects and AD patients in the low-frequency EEG subbands. Hence, in the proposed method TF representations of all EEG subbands are used for feature calculation separately. TF energy distributions obtained by SST and STFT approaches are used to calculate 13 TF features to gather distinctive information between EEG segments of control subjects and AD patients. Various classification techniques are utilized to distinguish feature sets of two the groups. Simulation results demonstrate that the proposed method achieve outstanding validation accuracy rates. © 2022 IEEE.

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