Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Deprem Anında ve Sonrasında Dönüşebilen Tasarım Önerileri: Günlük Kullanımdan Hayat Kurtaran Birimlere(Afet ve Acil Durum Yonetimi Baskanligi (AFAD), 2025-12-30) Özmen, Nihan Muş; Kurtuluş, Vacide BetülDepremler, yıkılan binalar ve devrilen eşyalar nedeniyle meydana gelen yaralanmalarla insan hayatını ciddi şekilde tehdit etmektedir. Bu çalışma, mobilyalara kendini kurtarma alanları entegre ederek ezilme yaralanmalarını önlemeyi ve deprem sonrası barınma ihtiyaçlarını karşılamayı amaçlayan tasarım önerileri sunmaktır. 2022 Güz döneminde Abdullah Gül Üniversitesi Deneysel Tasarım Stüdyosu kapsamında yürütülen çalışma, tanınmış bir mobilya tasarım firmasıyla iş birliği içinde gerçekleştirilmiştir. Öğrenciler hem günlük yaşamda işlevsel hem de afet anlarında acil barınma alanı olarak kullanılabilecek çift amaçlı mobilya tasarımları geliştirmiştir. Projeler, deprem sırasında ve sonrasında kullanılmak üzere iki ana kategoriye ayrılmış; yaşam ve çalışma mekânlarında, dönüşebilir mobilya tasarımlarına odaklanmıştır. Bulgular, işlevsel ve uyarlanabilir tasarımlar yoluyla risk azaltma ve deprem sonrası uyum konularında yenilikçi yaklaşımları ortaya koymaktadır.Article Citation - Scopus: 1Kayseri İlindeki Bazı Tarihi Eserlerde Bozunma Etkilerinin Tahribatsız Deney Yöntemleriyle Değerlendirilmesi(TMMOB Chamber of Geological Engineers, 2025-06-11) Akin, Mutluhan; Akin, Muge; Akgül, Muhammed Kamilİç Anadolu’da önemli bir yerleşim merkezi olan Kayseri, farklı dönemlerden günümüze kadar gelen birçok tarihi esere ev sahipliği yapmaktadır. İlin farklı bölgelerinde özellikle yoğun yerleşimin bulunduğu alanlarda, Selçuklu Dönemi’ne ait 12. ve 14. yüzyıllar arası yapılmış çok sayıda cami, kümbet ve medrese türü tarihi esere rastlamak mümkündür. Kültürel miras niteliğindeki bu eserler çoğunlukla yakın çevrede yoğun olarak bulunan farklı renk ve dokudaki ignimbirit türü kaya malzemesi kullanılarak inşa edilmişlerdir. Genel olarak düşük dayanıma sahip ve su etkilerine karşı hassas olan bu ignimbiritler zaman içerisinde atmosferik etkenler, hava kirliği, vandalizm vb. gibi olaylar sonucunda bozunmakta ve ilksel özelliklerini kaybetmektedirler. Bu çalışmada Kayseri il merkezindeki Roma ve Selçuklu dönemlerine ait tarihi eserler ile bu eserlerde zaman içinde meydana gelen bozunma etkileri incelenmiştir. Bozunma etkilerinin gözlemsel olarak incelenmesinin yanı sıra, eserlere herhangi bir zararı bulunmayan tahribatsız deney yöntemlerinden İğne Penetrometresi, Schmidt Çekici ve P-dalga hızı deneylerinden faydalanılmıştır. Bunun yanı sıra, bozunmuş ignimbirit bloklarına ait yerinde deneylerle belirlenen değerler, aynı malzemeye ait taze örneklerin fiziko-mekanik özellikleri ile karşılaştırılmıştır. Yapılan değerlendirmeler sonucunda tarihi eserlerin taban bölümlerinde özellikle kılcallık sebebiyle pullanma ve kavlaklanma türü bozunmaların geliştiği ve ignimbiritlerin bu bölümlerde dayanımlarını önemli ölçüde kaybettiği tespit edilmiştir. İncelenen kümbetlerin bazılarına uygulanan iyileştirme çalışmalarında ise kümbetlerin çevresinde bulunan yüzey suyu drenajlarının yeterli ölçüde yapılamadığı ve yağmur ile biriken suların tarihi eserlerin daha fazla bozunmasına sebep olduğu saptanmıştır. Kültürel miras olarak değerlendirilen bu tarihi yapıların korunup gelecek nesillere aktarılması amacıyla, ignimbirit yapılarının yüzeysularına karşı duyarlılığı dikkate alınarak tarihi kümbetlerin çevresinde su drenajı iyileştirme çalışmaları yapılması önerilmektedir.Conference Object Citation - WoS: 1Citation - Scopus: 1Prediction of Type 2 Diabetes Using Metagenomic Data and Identification of Taxonomic Biomarkers(IEEE, 2024-05-15) Temiz, Mustafa; Kuzudisli, Cihan; Yousef, Malik; Bakir-Gungor, BurcuNowadays, different molecular levels of -omics data on diseases are generated and analyzing these data with machine learning methods is one of the popular research topics. Among these data, the use of metagenomic data to facilitate the diagnosis, detection and treatment of diseases is increasing day by day. Type 2 diabetes (T2D) is a chronic disease characterized by insulin resistance and progressive dysfunction of pancreatic beta cells. While the number of people with diabetes is increasing by around 8% annually, the cost of treating the disease is rising by 18% per year. Therefore, the number of studies on the diagnosis, development and progression of T2D is increasing over time. The aim of this study is to achieve higher machine learning performance by using fewer metagenomic features and to achieve better classification performance by reducing computational costs. In this study, we compare the performance of three different methods using T2D-related metagenomic data. First, the MetaPhlAn tool is used to calculate the taxonomic species and their relative abundances in each sample. The SVM-RCE, RCE-IFE and microBiomeGSM tools used in this study are methods that perform classification by grouping and scoring features and are known to work well on complex datasets. In this study, the best results were obtained with the RCE-IFE tool with an AUC of 0.72 with an average of 125 features information. In addition, key taxonomic species identified by these tools as associated with T2D are presented in comparison to the literature.Conference Object Citation - Scopus: 1PCB Component Recognition With Semi-Supervised Image Clustering(IEEE, 2021-06-09) Unal, Ahmet Emin; Tasdemir, Kasim; Bahcebasi, AkifClassification 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.Conference Object Graph-Based Biomedical Knowledge Discovery(IEEE, 2024-05-15) Altuner, Osman; Bakir-Gungor, Burcu; Bakal, GokhanThe digitalization process is progressing at a very high speed all over the world. While this situation provides many conveniences in today's life, it also brings along a problem such as analyzing and processing the huge digital data. This also applies to published academic studies. In this sense, the process of evaluating each study to access previously unknown information within the studies requires a very laborious process. For this reason, in this study, the publications obtained for the target diseases were analyzed by text analysis processes and converted into a graph structure that enables the linking of meaningful terms through biomedical relationships. On the dense graph structure obtained, binary biomedical entities with important links such as treats, causes, associated_with were queried. The entity pairs obtained according to the query results were also confirmed by manual search method and proved to be real connections. In this study, retrieval of known biomedical entities with the proposed approach solved the time-consuming manual search problem. There is also the potential to obtain unknown/unexplored possible new relationships (e.g., therapeutic, causal, etc.) with multiple binary linking patterns.Conference Object Citation - WoS: 10Citation - Scopus: 16Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO(IEEE, 2022-05-15) Kabas, BilalIn 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*.Article 3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging(Gazi Univ, Fac Engineering Architecture, 2025-02-03) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, BulentThis 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.Article Citation - Scopus: 2Vs (30) Tabanlı Yerel Zemin Koşulları Ve Deprem Hasar İlişkisi: Van-Abdurrahmangazi Örneği(TMMOB Chamber of Geological Engineers, 2021-12-31) Aykaç, Zeynep; Akin, Muge K.; Çabalar, Ali FiratIn order to minimize the disaster risk caused by earthquakes, not only province and district-based studies, but also studies covering small areas such as neighborhoods and villages should be carried out. In this study, Abdurrahmangazi Neighborhood, one of the districts that was severely damaged by two earthquakes that took place on 23 October and 09 November 2011 in the province of Van, was examined. The building conditions and the ground conditions in the study area where the quarter is located have been considered together. Shear wave velocity (V<inf>s</inf>) was used to determine the dynamic behavior of soils. The borehole data obtained in the study area were evaluated and the shear wave velocities were determined by using 5 different empirical relations developed by some researchers for the relationship between SPT-N and V<inf>s</inf> . Using these, V<inf>S(30)</inf> values were determined and ground classifications were made according to the National Earthquake Hazard Reduction Program (NEHRP-2000), EUROCODE-8, the Regulation on Buildings to be Built in Earthquake Zones (DBYBHY-2007). In addition, the new earthquake regulations is Turkey Earthquake Building Regulations (TBDY-2018) were also considered. The building damage conditions and the ground conditions in the area where the quarter is located were evaluated together. It was determined that the building damages after earthquakes were caused by structural deficiencies and building quality for this neighborhood, regardless of the ground conditions, and damage distributions were interpreted accordingly. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1The Identification of Discriminative Single Nucleotide Polymorphism Sets for the Classification of Behcet's Disease(IEEE, 2018-09) Gormez, Yasin; Isik, Yunus Emre; Bakir-Gungor, BurcuBehcet's disease is a long-term multisystem inflammatory disorder, characterized by recurrent attacks affecting several organs. As the genotyping individuals get cheaper and easier following the developments in genomic technologies, 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) are identified. Although several genetic risk factors are identified for Behcet's disease with the help of these studies via scanning around a million of SNPs, these variations could only explain up to 200/u of the disease's genetic risk. In this study, for Behcet's disease classification, via comparing all the SNPs genotyped in GWAS, with the SNPs selected via using genetic knowledge, gain ratio and information gain; both reduction in the feature size and improvement in the classification accuracy is aimed. Also, using different classification algorithms such as random forest, k-nearest neighbour and logistic regression, their effects on the classification accuracy are investigated. Our results showed that compared to other feature selection methods, with at least 81% success rate, the selection of the SNPs using the genetic information (of their GWAS p-values, indicating the significance of the SNP against the disease) provides 15% to 42% improvement in all classification algorithms. This improvement is statistically sound. While gain ratio and information gain feature selection techniques yield similar classification accuracies, the models using all SNPs could not exceed 50% accuracies and results in the worst performance.Conference Object Citation - Scopus: 2Kısa Ve Orta Mesafe Gece Yangını Tespiti(Institute of Electrical and Electronics Engineers Inc., 2017-05) Agirman, Ahmet K.; Taşdemir, Kasím; Aggirman, Ahmet KerimComputer vision methods used for night-time fire detection are limited. Existing works are for detection of distant night fires recorded from watch towers. In this paper, detection of short to mid-range night fires from video cameras are aimed. Flames in short distance flicker, grow and move more rapidly compared to ones in long distance. Features obtained by taking advantage of these distinctions let us detect fire over 90% accuracy on average in videos containing deceptive light sources like common city lights and headlights of vehicles. © 2017 Elsevier B.V., All rights reserved.