Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/3

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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.
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 - 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.
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.
Attitudes of Nursing Senior Students Towards the Use of Computers in Healthcare and Related Factors
(Association of Executive Nurses, 2022) Söylemez, B.A.; Özgül, E.; Akyol, M.A.; Küçükgüçlü, Ö.
Aim: This study was conducted to determine the attitudes of nursing senior students towards the use of computers in healthcare and related factors. Method: The descriptive and cross-sectional study was conducted with 162 senior nursing students in a faculty of nursing at a university between June and July 2021. Data were collected with the “Participant Information Form” and “Attitudes toward Computers in Healthcare Assessment Scale.” The SPSS 25.0 package program was used to evaluate the data. Socio-demographic data were given as numbers, mean, percentages, and standard deviation. Number, mean, percentage distributions, independent groups t-test, Mann Whitney-U test, One-way ANOVA test, and Pearson correlation test were used to analyze the data. Results: In this research, 67.9% of the 162 students were females, and the mean age was 22.43±1.50 years. The mean score of the students on the scale was 15.65±8.91. Status of owning a computer (t=2.729, p<0.01), frequency of computer usage (u=637.500, p<0.01), level of knowledge in using a computer (F=13.410, p<0.001), and status of computer use in nursing practices (t=4.244, p<0.001) were found to affect attitudes of nursing students towards the use of computers in healthcare. Conclusion: Senior nursing students were found to have a moderate attitude towards using computers in healthcare. Adopting more positive attitudes towards this area will increase the quality of nursing care and provide easier access to clinical data and charts. © 2022 The Authors.
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
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.
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
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.
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
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.
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.
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.
Citation - Scopus: 2
Classification of Epileptic Eeg Signals Using Dynamic Mode Decomposition
(Institute of Electrical and Electronics Engineers Inc., 2020) Cura O.K.; Pehlivan S.; Akan A.
In the literature, several signal processing techniques have been used to diagnose epilepsy which is a nervous system disease. However most of these techniques fail to analyse EEG signals which are dynamic and non-linear. In this study, an approach which utilizes a data-driven technique called Dynamic Mode Decomposition (DMD) that was originally developed to be used in fluid mechanics was proposed. Features that were belonged to EEG signals were calculated using DMD method and with the help of different classifiers, classification of the preseizure and seizure EEG signals was performed. Obtained results showed that the proposed method presented an alternative to approaches that are based on Empirical Mode Decomposition and its derivatives. © 2020 IEEE.
Citation - Scopus: 3
Clinical Impact of Hepatitis C Virus Genomic Variations
(Ankara Microbiology Society, 2015) Ergünay K.; Abacioglu H.
Hepatitis C virus (HCV) is a globally-dispersed agent of chronic hepatitis with a significant public health threat, affecting over 110 million individuals throughout the world. The increased risk for chronicity after exposure and the lack of a protective vaccine make HCV is a leading infectious cause of cirrhosis, liver failure requiring transplantation and hepatocellular carcinoma. The replicative process and infection dynamics in the host enable HCV to generate an array of closely-related but non-identical genetic variants known as quasispecies in the infected individuals. Pathogenesis and outcome in HCV infections are directly affected by the virus genetic heterogeneity, reflected as the emergence of quasispecies in infected individuals. The evolution of these highly-diverse viral populations in the host directly influences the disease course, via providing a pool of variants capable of resuming viral replication under extrinsic and/or intrinsic selective pressures. Viral quasispecies go through several alterations during the course of the infection, and provide a background for the selection of escape mutants from the host humoral and cell-mediated immune responses and antiviral treatment. Supported by the robust next generation sequencing techniques, recent studies have provided significant insights on the genomic diversity and progression as well as on the origin and the epidemiology of HCV. This review provides an overview of the mechanisms of HCV genetic variability, and the interactions with the host, that affects clinical disease, covering viral and host determinants of humoral and cell-mediated immune responses, alterations during the early and late stages of the infection and disease progression leading to chronicity. In addition, current findings in virus evolution and epidemiology were briefly interpreted from the inter-species and population perspectives. The impact of viral genomic heterogeneity on antiviral treatment in the era of direct-acting agents is also discussed, along with an overview of current methods employed for the characterization of viral diversity.
Çocuklar İçin Yaşamsal İşaretleri Takip Eden Ekosistem Tasarımı
(IEEE, 2024) Akbugday, Burak; Kizil, Melahat; Akan, Aydin
In this study, a conceptual framework which enables tracking of vital signals of children and aids the diagnosis process by medical instituions that includes various software and hardware components is proposed. The ecosystem includes a smartband that has wireless communication capabilities and has photophyletismograph (PPG) and temperature (TEMP) sensors as well as a mobile and desktop app, and finally a cloud-based artificial intelligence (AI) system. The proposed ecosystem aims combining the vitals tracked by the smartband with the existing medical information kept by the medical institutions and utilizes extreme gradient boosting (XGBoost) algorithm to predict medical conditions with high accuracy. Furthermore, the use of lowcost, power efficient and sustainable hardware targets the widespread use of the ecosystem in resource-limited environments. The conceptually designed proposed system's realizability with the existing hardware as well as its strenghts in comparison to the existing systems is demonstrated.
Çoklu Biyosensör Dizilerinin Nesnelerin İnterneti ile Uzaktan İzlenmesi için Altyapı Oluşturulması
(IEEE, 2024) Topalli, Ayca Kumluca
The number of telemetry applications based on Internet of Things is increasing rapidly. These researches are generally about biosensor design; but seamless data transmission is also important. This study is about to construct a robust infrastructure in case of a busy data transfer traffic simultaneously from many biosensors at many different locations to the same server, to setup a reliable platform for data storing, processing, and presenting with different interfaces. The system where Microsoft Azure infrastructure is used is tested by 100 different data input simultaneously. Proposed system offers a solution for health care services such as incubator monitoring, remote monitoring of elderly, etc.
Citation - Scopus: 1
A Comparative Study of Compressed Sensing Video Encoding Gop Patterns for Stereo Distributed Video Coding
(2012) Aslan S.; Tunalı, Turhan
In this study, compressed sensing concepts are applied to multi-view video coding. Existing work from single view video is utilized to develop efficient GOP patterns and reference framing for stereo coding. It has been observed that the most typical choice of pattern improved the characteristics 0.4 dB with respect to the model that do not benefit from interview sparsity for all frames. Alternatives for future work are proposed. © 2012 IEEE.
Citation - Scopus: 2
Comparison of Wavelet Based Feature Extraction Methods for Speech/Music Discrimination [conference Object]
(2010) Düzenli T.; Özkurt N.
In this study, performance of wavelet transform based features for the speech / music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features used in speech/music discrimination such as number of zero crossings, spectral centroid, spectral flux and mel cepstral coefficients. In order to measure the performances of the feature sets for the speech/music discrimination, artificial neural networks have been used as a classification tool. The principal component analysis has been applied to eliminate the correlated features before classification stage. Considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. According to the results the proposed feature set outperforms the traditional ones.
Concept Design Of Supportive Mechanism For Foldable Stretchers Used İn Ambulances;
(Institute of Electrical and Electronics Engineers Inc., 2024) Üstünkarlı, Nursun; Kizil, Melahat; Türkan, Murat; Dülger, Lale Canan
Ambulance workers face many risks arising from the nature of the service they provide. One of these risks is musculoskeletal injuries. Manual use of ambulance stretchers requires transporting seriously ill patients, which can cause occupational diseases for pre-hospital medical staff. The aim of this study is to develop mechanism that can be placed on an existing ambulance stretcher so that the stretcher can be lifted and lowered without the need for manpower. This apparatus uses a rechargeable system without disrupting the mechanism of the stretcher. © 2024 IEEE.
Contribution of Syntactic and Semantic Attributes in Paraphrase Identification
(Institute of Electrical and Electronics Engineers Inc., 2018) Karaoglan B.; Kisla T.; Metin S.K.
Automatic paraphrase identification is a natural language understanding problem where a decision is to be made whether the given sentence pairs bare similar meanings to a certain extent. Syntactic and semantic features are used to classify the sentences as paraphrase or non-paraphrase. Word overlapping, word ordering are some of the syntactic features widely used in the literature, where, similarity of words in meaning and named entity (NE) overlap are among the semantic features. Turkish, unfortunately doesn't have a useful tool like WordNet to draw the semantic relations between words as it is done for English. Here we exploit tense and polarity differences as semantic features and assess the improvement on the classification brought by these semantic features. We performed the experiments with several different combinations of features on the Turkish paraphrase corpus that is built by the researchers and report the results. © 2018 IEEE.

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