WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Citation - WoS: 1
Citation - Scopus: 3
Endüstriyel Kablosuz Algılayıcı Ağlarda Hata Kontrol Sistemlerinin Ağ Yaşam Süresine Etkileri
(IEEE, 2019) Tekin, Nazli; Gungor, V. Cagri
Due to the harsh channel conditions of the industrial environment, the data transmission over wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of vital importance for battery-powered Wireless Sensor Networks (WSNs). In this paper, the performance evaluation of error control schemes namely, Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ) in industrial environment in terms of energy efficiency is presented. The impact of the existing error control schemes on the industrial wireless sensor network lifetime is analyzed. A novel Mixed Integer Programming (MIP) framework is developed to maximize network lifetime. Performance results show that utilizing BCH (31,21,5) for Telos at the link layer maximizes the network lifetime while attaining the desired application reliability rate.
Performance Evaluations of Active Subnetwork Search Methods in Protein-Protein Interaction Networks
(IEEE, 2019) Gunter, Pinar; Bakir-Gungor, Burcu
Protein-protein interaction networks are mathematical representations of the physical contacts between proteins in the cell. A group of interconnected proteins in a protein-protein interaction network that contains most of the disease associated proteins and some interacting other proteins is called an active subnetwork. Active subnetwork search is important to understand mechanisms underlying diseases. Active subnetworks are used to discover disease related regulatory pathways, functional modules and to classify diseases. In the literature there arc many methods to search for active subnetworks. The purpose of this study is to compare the performance of different subnetwork identification methods. By using the Rheumatoid Arthritis dataset, the performances of greedy approach, genetic algorithm, simulated annealing algorithm, prize collecting steiner forest and game theory based subnetwork search methods are compared.
Citation - Scopus: 3
LTE Ağları için Servis Kalitesi Farkında Aşağı Yönlü Çizelgeleme Algoritması: Kenar Kullanıcıları Üzerine İnceleme
(Institute of Electrical and Electronics Engineers Inc., 2017) Güngör, Vehbi Çağrı; Uyan, Osman Gokhan
4G/LTE (Long Term Evolution) is the state of the art wireless mobile broadband technology. It makes use of the OFDM technology to offer high speed and provides the system resources both in time and frequency domain. A scheduling algorithm running on the base station holds the allocation of these resources. In this paper, the performance of existing downlink scheduling algorithms has been investigated in two ways. First, the performance of the algorithms has been investigated in terms of throughput and fairness metrics. Second, a new quality of service-aware (QoS-aware) fairness criterion, which accepts that the system is fair if it can provide the users with the network traffic speeds that they demand, has been proposed and evaluated. In addition, a novel QoS-aware downlink-scheduling algorithm, which increases the QoS-aware fairness and overall throughput of the edge users, has been proposed. © 2017 Elsevier B.V., All rights reserved.
Citation - WoS: 1
Citation - Scopus: 1
Kolon Kanserinde Etkilenen Yolak Alt Ağlarini Ve Kümelenmelerini Belirlemek için Yeni Bir Yöntem
(Institute of Electrical and Electronics Engineers Inc., 2019) Göy, Gökhan; Ünlü Yazici, Miray; Bakir-Güngör, Burcu
Nowadays new technological developments that play an important role in the production of big data have brought about the interpretation, sharing and storage of data related to complex diseases. Combining multi-omic data in different molecular levels is potentially important for understanding the biological origin of complex diseases. One of these complex diseases is cancer of different types, which has one of the highest causes of death worldwide. The integration of multiple omic data in the framework of a comprehensive analysis and identification of relevant pathways contribute to the development of therapeutic approaches related to disease. In this study, RNA and methylation data (genes and p values) of colon adenocarcinoma were obtained from TCGA data portal and combined with Fisher's method. While protein subnetworks affected by the disease were identified by using subnetwork algorithm, pathways related to the disease and genes associated with these pathways were determined by functional enrichment analysis. Using gene-pathway relationship matrix, kappa scores of pathways were determined by similarity calculation. In this way, the pathways were clustered according to the hierarchically optimal number, as a result, the most important pathway clusters and related genes that are effective in disease formation identified. © 2020 Elsevier B.V., All rights reserved.
Citation - Scopus: 3
Protein İkincil Yapı Tahmini Için Makine Öǧrenmesi Yöntemlerinin Karşılaştırılması
(Institute of Electrical and Electronics Engineers Inc., 2018) Aydin, Zafer; Kaynar, Oǧuz; Görmez, Yasin; Işik, Yunus Emre
Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Due to rapidly growing databases and recent feature extraction methods datasets used for predicting secondary structure can potentially contain a large number of samples and dimensions. For this reason, it is important to use algorithms that are fast and accurate. In this study, various classification algorithms have been optimized for the second phase of a two-stage classifier on EVAset benchmark both in the original input space and in the space reduced using the information gain metric. The most accurate classifier is obtained as the support vector machine while the extreme learning machine is significantly faster in model training. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Koroner Arter Hastalığı Tanısı İçin Alan Bilgisi İçeren Topluluk Öznitelik Seçim Yöntemi
(Institute of Electrical and Electronics Engineers Inc., 2020) Kolukisa, Burak; Güngör, Vehbi Çağrı; Bakir-Güngör, Burcu
Coronary Artery Disease (CAD) is the condition where, the heart is not fed enough as a result of the accumulation of fatty matter called atheroma in the walls of the arteries. In 2016, CAD accounts for 31% (17.9 million) of the world's total deaths and its diagnosis is difficult. It is estimated that approximately 23.6 million people will die from this disease in 2030. With the development of machine learning and data mining techniques, it might be possible to diagnose CAD inexpensively and easily via examining some physical and biochemical values. In this study, for the CAD classification problem, a novel ensemble feature selection methodology that incorporates domain knowledge is proposed. Via applying the proposed methodology on the UCI Cleveland CAD dataset and using different classification algorithms, performance metrics are compared. It is shown that in our experiments, when Multilayer Perceptron classifier is used with 9 selected features, our proposed solution reached 85.47% accuracy, 82.96% accuracy and 0.839 F-Measure. As a future work, we aim to generate a machine learning model that can quickly diagnose CAD on real-time data in hospitals. © 2021 Elsevier B.V., All rights reserved.
Ball Lens Based Mobile Microscope
(Gazi Univ, 2016) Icoz, Kutay
In this paper we report a low cost, simple and mobile microscope based on attachment of a ball lens to a cell phone. The system's noise and parameters affecting the image quality is investigated. The ball lens provides approximately 100X magnification and together with the cell phone's integrated lens and image sensor, 3,4-micron resolution is reached. The field-of-view of the system is 1500x1500 mu m where the price of the ball lens and the holder is less than 10 cents. By using this system as an optical light microscope, we are able to acquire images of micro particles and micro sensors. When combined with image processing methods, this optical system is capable of doing complex analysis as an alternative to commercial optical light microscopes.
Kolonoskopi Görüntülerinden Otomatik Ülseratif Kolit Teşhisi
(Institute of Electrical and Electronics Engineers Inc., 2018) Kacmaz, Rukiye Nur; Yilmaz, Bulent
Ulcerative colitis (UC) is a disease in which inner surface of colon is inflamed. Ulcers and open scars on the colon are observed. The complaint in the flare period is the frequent bloody diarrhea. Complaints of people with UC increase and decrease periodically. Colonoscopy is the most preferred approach for the visualization of the gastrointestinal tract for the diagnosis and follow-up of related diseases, and UC in particular. The lack of experience of the colonoscopist, complicated locality of the lesion, and the rush in the colonoscopy suite to complete the procedure as soon as possible may cause mistakes in visual analysis. In this study, 200 colonoscopy images (100 normal, 100 UC) were used. The statistical features such as gray level variance, gray level local variance, normalized variance, histogram range, and entropy were extracted from the images, and a normalized 200x5 feature matrix was formed. The normal images and images with UC were discriminated using support vector machines and k-nearest neighbors. It should be noted that the extraction of only 5 features from the colonoscopy images resulted in 95% accuracy. This study demonstrated the feasibility of the development of software tools for aiding the physicians in the diagnosis of colon diseases. © 2019 Elsevier B.V., All rights reserved.
Citation - WoS: 5
Enerji Hasadı Yapan Sualtı Kablosuz Duyarga Düğümlerinin Yaşam Ömrü Analizi
(IEEE, 2017) Erdem, H. Emre; Gungor, V. Cagri
The application of Wireless Sensor Networks (WSNs) in underwater environments poses various challenges. One of the most important problems is the limited lifetime of underwater sensor nodes. Considering how challenging and costly it is to change the batteries of sensor nodes in underwater environments, energy harvesting methods arc rendered as a promising solution. In this study, the contributions of energy harvesting via turbine and hydrophone harvesters as well as schedule and trigger driven energy management methods on node lifetime have been analyzed. Performance evaluations have been conducted considering real-life conditions, e.g. flow rates, of Istanbul Bosphorus Strait.
Elastic Modulus Prediction for Fiber-Reinforced Concretes
(Pamukkale Univ, 2020) Yagmur, Eren
In this study, the effects of different discrete fiber types on the elastic modulus of concrete are investigated. For this purpose, 260 cylindrical pressure test specimens are compiled. The fiber types considered are steel, PVA, polypropylene, polyolefin, basalt and olefin. The results of the study are showed that if the ratio of coarse aggregate to fine aggregate exceeds 1.5 for all fiber types, the compressive strength of concrete decreases. It has been observed that the elastic modulus increases in cases where the fiber aspect ratio of the steel fibers is less than and equal to 60, while the elastic modulus decreases for values greater than 60. An elastic modulus equation, which applies to all fiber types considered, is proposed. The proposed equation is compared with the experimental results and the other formulas in the literature and the validity of the equations for different cases are questioned.
Citation - WoS: 4
Blockchain-Based Fog Computing Applications in Healthcare
(IEEE, 2020) Adanur, Beyhan; Bakir-Gungor, Burcu; Soran, Ahmet
Recently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed.
3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
(Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
İmmün Bağlantılı Hastalıklarda Aktif Alt Ağ Araması ile Ortak Hastalık Oluşum Mekanizmalarının Tespiti
(IEEE, 2020) Eryilmaz, Mahmut Kaan; Kuzudisli, Cihan; Gungor, Burcu Bakir
Different, but related diseases often contain shared symptoms indicating the presence of possible overlaps in underlying pathogenic mechanisms. The identification of the shared pathways and related factors across these diseases helps to better understand the causes of these diseases, to prevent and treat these diseases. In this study, using immune-related diseases, we proposed a new method on how to compare the development mechanisms of related diseases based on biological pathways. Following the developments in genomic technologies, the genotyping gets cheaper and easier, and hence genome-wide association studies (GWAS) emerged. By this means, via studying big-sized case-control groups for a specific disease, potential genetic variations, single nucleotide polymorphisms (SNPs) could he identified. With the help of these studies, in which around a million of SNPs are scanned, the variations and genes that could have a role in specific disease development could be detected. In this study, via using available GWAS datasets and human protein-protein interaction network, and via detecting active subnetworks and affected pathways, seven immune related diseases are analyzed. Via investigating the similarities among the identified pathways for related diseases, we aim to define the underlying pathogenic mechanisms, and hence to contribute to the elucidation of disease development mechanisms and to the drug repositioning studies.
Lifetime Analysis of Underwater Wireless Networks Concerning Privacy With Energy Harvesting and Compressive Sensing
(IEEE, 2019) Uyan, O. Gokhan; Gungor, V. Cagri
Underwater sensor networks (UWSN) are a division of classical wireless sensor networks (WSN), which are designed to accomplish both military and civil operations, such as invasion detection and underwater life monitoring. Underwater sensor nodes operate using the energy provided by integrated limited batteries, and it is a serious challenge to replace the battery under the water especially in harsh conditions with a high number of sensor nodes. Here, energy efficiency confronts as a very important issue. Besides energy efficiency, data privacy is another essential topic since UWSN typically generate delicate sensing data. UWSN can be vulnerable to silent positioning and listening, which is injecting similar adversary nodes into close locations to the network to sniff transmitted data. In this paper, we discuss the usage of compressive sensing (CS) and energy harvesting (EH) to improve the lifetime of the network whilst we suggest a novel encryption decision method to maintain privacy of UWSN. We also deploy a Mixed Integer Programming (MIP) model to optimize the encryption decision cases which leads to an improved network lifetime.
Citation - Scopus: 2
Beyin Dalgalari ve Baş Hareketiyle Gerçek Zamanli Robotik Araba Kontrolü
(Institute of Electrical and Electronics Engineers Inc., 2018) Oztürk, Nedime; Yilmaz, Bulent; Onver, Ahmet Yasin
Emotiv Epoc Headset is a portable and low-cost device. In this study, Emotiv Epoc headset was used in order to obtain real-time gyro and EEG signals. The aim of this study was to control a robotic car in real-time by using head movement and opening and closing of the eyes. The maximum and minimum amplitude of the gyro signal, and the ratios of the beta waves of O1 and O2 channel to alpha waves of the same channels were used as threshold values. These threshold values were used to determine the direction of the robotic car. Because of its low-cost and easy implementation, Arduino Uno was used to manage the robotic car. This study has shown that brain waves and head movements can control a device in real time. This system has the potential to be used in neurofeedback and brain-computer interface applications. © 2019 Elsevier B.V., All rights reserved.
Citation - WoS: 6
Citation - Scopus: 11
Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO
(IEEE, 2022) Kabas, Bilal
In this paper, a computer vision-based navigation system is proposed for autonomous unmanned aerial vehicles (UAV). The proposed navigation system is based on a deep reinforcement learning-based high-level controller. In this paper, proximal policy optimization (PPO), which is a deep reinforcement learning method, is used to train the artificial neural network in an end-to-end way using a continuous reward function. The proposed method has been tested on images obtained from different modalities (RGB and depth) in simulation environments that are created using Unreal Engine and Microsoft AirSim. For the navigation problem that this work is concerned with, a success rate of 96% has been obtained by using RGB cameras. Since RGB cameras are lighter than depth cameras and the trained artificial neural network has a parameter number less than 170.000, the proposed method is suitable to be deployed in micro aerial vehicles. Code is publicly available*.
Kolonoskopi Görüntülerinde Bilineer İnterpolasyonun Tekstör Analizine Etkisi
(Institute of Electrical and Electronics Engineers Inc., 2017) Kacmaz, Rukiye Nur; Yilmaz, Bulent
Interpolation is a method that is used to obtain unknown intensities with the help of known intensities on an image. This method is frequently used in the literature to eliminate light reflection on colonoscopy images. Texture features are the most important characteristics used to describe the region or objects of interest in the image. They are the measures of intensity variation of a surface that determine properties such as smoothness, roughness, and regularity. The aim of this study is to find out the how bilinear interpolation applied on colonoscopy images with reflection impact texture features obtained from the same images. A research carried out to make reasonable comparison between a texture feature from an image with no reflection and the same feature obtained from the same image with synthetically added reflections with various percentages. Using the approaches like gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM) 126 features were extracted from each 32×32 sub-images coming from 610 colonoscopy images. Several of the features extracted from sub-images with no reflection and reflection were not statistically significantly different, while majority of them were affected from the reflections. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Protein İkincil Yapı Tahmini için NR ve UniClust Veri Tabanlarının Karşılaştırılması
(Institute of Electrical and Electronics Engineers Inc., 2018) Aydin, Zafer; Kaynar, Oǧuz; Görmez, Yasin
Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Improving the accuracy rate in protein secondary structure prediction depends on computed attributes as well as the classification algorithms. In multiple alignment methods, which are often used to extract an attribute, the calculated values differ according to the database used for the alignment. For this reason, it is important to use a suitable database against which the target proteins are aligned to compute profile feature vectors. In this study, 5 different datasets are generated for the CB513 benchmark with the aid of two different alignment methods and three different databases. The profile features are fed as input to a two-stage hybrid classifier. According to the experimental results, the highest accuracy rate is obtained when UniClust database is used at the first stage of HHBlits alignment to calculate PSSM values and NR database is used at the first stage of HHBlits alignment to calculate structural profile matrices. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
PCB Component Recognition With Semi-Supervised Image Clustering
(IEEE, 2021) Unal, Ahmet Emin; Tasdemir, Kasim; Bahcebasi, Akif
Classification of surface mounted devices plays an important role on automated inspection systems of printed component board production. Limited number of publicly available datasets which the components are labeled and high intraclass variance in these datasets causes the supervised approches to be inefficient. In this study a deep learning method, enhanced with an unsupervised clustering system, which uses a small set of labeled data is proposed. The method compared with the current studies and the supervised systems. Most optimized setting reached high accuracy results by outrunning current classification methods.
Citation - WoS: 2
Reconsidering the Changing Higher Education System from Sociocultural and Spatial Perspectives
(Deomed Publ, Istanbul, 2020) Ayten, Asim Mustafa; Gover, Ibrahim Hakan
Education and research are vital for social development and progress. The changing sociocultural structures and new needs have resulted in some important functional changes in higher education systems with a deep impact on universities serving these needs at the highest level. Besides experiencing these functional changes, the universities today have become spaces of socialization with their social, cultural and sports facilities, replacing their traditional spatial role of offering education only. The local dynamics changing with globalization have now reshaped the global and local roles of universities, highlighting the added value they provide to the society. Sociocultural changes trigger all these functional and structural changes in universities. Therefore, sociocultural factors and their importance should not be ignored in a changing higher education system. In this study, the impact of sociocultural factors with their related spatial structures on world higher education system will be analyzed within their historical contexts, and some suggestions for future universities will be offered considering the current changes. In the first part of the study, the changes in societies and universities will be presented within the historical context. In the second part, the spatial forms and structures of universities will be discussed. In the third part, the catalytic effects of the specific sociocultural factors will be highlighted and elaborated on. Finally, some suggestions will be made for the universities of the future in the light of the current situation and the data available.

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