Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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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.
Citation - Scopus: 10
Sağlıkta Blokzincir Tabanlı Sistem Bilişimi Uygulamaları
(Institute of Electrical and Electronics Engineers Inc., 2020) Dedeturk, Beyhan Adanur; Bakir-Güngör, 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. © 2021 Elsevier B.V., All rights reserved.
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 - Scopus: 3
Türkiye’de Yapılan Kuraklık Analiz Çalışmaları Üzerine Bir Derleme
(Ankara University, 2022) Deniz Öztürk, Yasemin; Ünlü, Ramazan
Drought has become one of the most studied disaster issues by scientists, especially after the 2000s, with the importance of climate change. Many scientific publications on drought have been produced, due to many different methods on drought and the study of drought by many disciplines of science. In the study, theses, national and international articles, which include drought analysis by using any statistical method over meteorological data in Turkey, were compiled. A total of 270 studies, including 73 master's and Ph.D. theses, 107 national articles, and 90 international articles, written between 1943-2021 were examined. These studies were classified according to the year of publication, the drought analysis methods used, in publication, the scientific field of the first author, and the region examined in the study, and their frequency distributions were revealed. The main conclusions of this study are as follows: Although the first published studies on drought analysis in Turkey were made in 1943, 1956, and 1965, studies on drought started to increase after 2000 and the total number of publications reached 37 in 2019, 43 in 2020, and 64 in 2021. Publications in the period of 2019-2021 correspond to 53% of all publications. This rapid increase in recent years has led to a logarithmic increase in the number of publications. Although 63 different methods are used in drought analysis in the studies, the standardized precipitation index is the dominant method with a usage rate of 56%. Most of the studies were carried out on the basins (113). In 41 studies, the whole of Turkey was examined. Other studies were carried out for geographical regions, provinces, and smaller settlements. According to the scientific fields, it is seen that the Civil Engineering (131 units) and Geography (41 units) departments are the scientific fields that carry out the most drought analysis studies. © 2025 Elsevier B.V., All rights reserved.
Manganzı Demir Cevherinden Manganın Çözündürülmesinde Farklı İndirgeme Maddelerinin Etkisi
(Chamber of Mining Engineers of Turkey, 2022) Top, S.; Altiner, Mahmut; Kursunoglu, Sait
IIn this paper, the manganese extraction from a manganiferous iron ore was investigated using reductive leaching. Various chemicals were used as a reducing agent to leach manganese selectively from the ore in the presence of sulfuric acid (H2SO4) solution. Firstly, optimum dissolution values were determined for selective manganese dissolution without using a reducing agent. As it was aimed at the selective extraction of manganese from the ore, the reductive leaching tests were conducted by adding the reducing agents under the following optimal parameters: a leaching time of 1 h, a stirring speed of 300 rpm, a temperature of 70°C, a sulfuric acid concentration of 1 M where the ore was leached with an extraction ratio of 11.54% Mn and 2.16% Fe. Manganese was dissolved with high efficiencies (up to 97.46%) from the ore by using different organic compounds (tartaric acid (C4H6O6), oxalic acid (C2H2O4), citric acid (C6H8O7), glucose (C6H12O6), sucrose (C12H22O11), and maleic acid (C4H4O4)) as the reducing agents. © 2022 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.
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 - Scopus: 2
Makine Öǧrenmesi Teknikleri Ile İnternet Servis Saǧlayıcısı için Müşteri Kayıp Tahmini
(Institute of Electrical and Electronics Engineers Inc., 2020) Göy, Gökhan; Kolukisa, Burak; Bahçevan, Cenk Anıl; Güngör, Vehbi Çağrı
With the developing technology in every fields, a competitive marketing environment has been arised. In this competitive environment, analyzing customer behavior has become vital. In particular, the ability to easily change any service provider has become very critical for the company to continue its existence. At the same time, the amount of financial resources spent on retaining customers much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities. In this study, we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value. In this context, the most successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936. © 2020 Elsevier B.V., All rights reserved.
Türkiye’de Farklı Yörelerde Bulunan Kolemanit Minerallerinin Fiziksel, Kimyasal Ve Termal Özelliklerinin Tayini
(Gumushane University, 2021) Senol-Arslan, Dilek
In this study, colemanite is an important boron mineral which constitutes about 76% of Turkey's boron reserves, is frequently used in applications obtained as a result of scientific and technological developments. In this context, a detailed literature survey was carried out colemanite minerals. Characteristic and structural features of the colemanite samples of four different regions (Kestelek, Emet (Hisarcık, Espey), Bigadiç regions) were determined by analyzes such as X-Ray Diffraction (XRD), Inductively Coupled Plasma, ICP and Mass Spectrometry, MS (ICP-MS), Thermogravimetric and Differential thermal analysis (TG-DTA), and Fouier Transform Infrared Spectroscopy (FT-IR) methods. In line with these findings, the mineralogical, chemical and thermal properties of pure colemanite crystals were determined and the similarities and differences between the samples were revealed. © 2025 Elsevier B.V., All rights reserved.
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.
Gazlaştırma Tesisi Odun Atığı ve Sivas Kangal Linyit Kömürünün Boya Adsorpsiyonunda Kullanım Olanaklarının Araştırılması
(Chamber of Mining Engineers of Turkey, 2020) Kırma, Ramazan; Sarikaya, Musa; Top, S.; Uçkun, Şükrü; Timür, İrfan
In this study, the usage possibilities of wood waste obtained from Gebze MDF and Particle Board Gasification Plant preliminary studies and Sivas Kangal lignite coal as absorbents were investigated. In this way, it was aimed both to evaluate the wastes and to prevent environmental pollution with materials that are cheaper and easier to obtain. The structure and surface properties of wood waste and coal samples crushed and ground to -75 µm size and used as adsorbent were investigated by XRD, SEM and BET analyses. In addition, samples have been characterized by elemental, ash, moisture, volatile matter and fixed carbon analyses. In the experiments, methylene blue (MM) with the formulation of C16H18CIN3S.xH2O was used. The effects of temperature, mixing time and concentration parameters on MM adsorption were investigated. Langmuir isotherms were created for different temperatures at optimum concentrations. As a result, it has been revealed that lignite coal and wood waste can be used as adsorbent. A 10 ppm MM for lignite coal and 3 ppm MM for wood waste were determined to be ideal concentrations for adsorption. © 2022 Elsevier B.V., All rights reserved.
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*.
Citation - Scopus: 5
Enerji Hasadı ve Sıkıştırmalı Algılama Yapan Gizlilik Odaklı Sualtı Kablosuz Ağlarında Ömür Analizi
(Institute of Electrical and Electronics Engineers Inc., 2019) Uyan, Osman Gokhan; Güngör, Vehbi Çağrı
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. © 2020 Elsevier B.V., All rights reserved.
Hepatoselüler Karsinom Oluşumunda Etkili Moleküler Mekanizmaların İn Siliko Yöntemlerle Araştırılması
(Institute of Electrical and Electronics Engineers Inc., 2020) Doǧan, Refika Sultan; Saka, Samed; Bakir-Güngör, Burcu
Hepatocellular carcinoma (HCC) is the most common cause of cancer-related death in the world. The molecular changes in the organism during the development of HCC are not fully understood. The aim of the present study is to contribute to the identification of critical genes and pathways associated with HCC via integrating various bioinformatics methods. In this study, experiments were conducted on gene expression data of 14 HCC tissues and noncancerous control tissues. A total of 1229 genes, which show a statistically significant change between the groups, were identified. Among these, 681 genes were upregulated and 548 genes were downregulated. Via mapping the detected genes into protein protein interaction networks, active subnetwork search, subnetwork topological analysis and functional enrichment analyses were carried out. The interactions between the molecular pathways affected by HCC were also presented. © 2020 Elsevier B.V., All rights reserved.
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.
Population Specific Classification of Colorectal Cancer With Meta-Analysis of Metagenomic Data
(Institute of Electrical and Electronics Engineers Inc., 2023) Temiz, Mustafa; Yousef, Malik; Bakir-Güngör, Burcu
Advances in next-generation sequencing and '-omics' technologies makes it possible to characterize the human gut microbiome. While some of these microorganisms are important regulators of our immune system, modulation of the microbiota leads to a variety of diseases. Colorectal cancer (CRC), the third most common cancer worldwide, is caused by genetic mutations, environmental conditions, and abnormalities in the gut microbiota. Using various machine learning methods and meta-analysis techniques, this study aims to build a classification model that can help in CRC diagnosis by analyzing metagenomic datasets of different populations obtained at the species level. Using 8 different countries and 9 different metagenomic datasets, 3 different meta-analyzes are performed: within-population, cross-population, and one population is selected for testing and the rest is used as a training dataset (LODO). For CRC classification, 4 different classification algorithms (Random Forest (RF), Logitboost, Adaboost, and Decision Tree (DT)) are used. The best performance among these methods was obtained with the Random Forest algorithm with an AUC of 0.98 by using JP for the training data set and JPN populations for the test data set in the cross-population performance evaluation. © 2023 Elsevier B.V., All rights reserved.

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