Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object High Performance and Resource Efficient Low Density Parity Check Decoder Design(IEEE, 2025-06-25) Unal, BurakLow Density Parity Check (LDPC) codes have gained popularity in communication systems due to their capacity-approaching error correction performance. In this study, a highperformance LDPC decoding algorithm with extremely low resource usage is proposed. Among the hard decision class of LDPC decoders, Gallager B (GaB) provides high-performance hardware due to its computational simplicity. However, GaB suffers from poor error-correction performance. In this study, a new intrinsic computation technique for GaB called Intrinsic Gallager B (IGaB) is introduced to improve error correction performance. Our simulation results show that the IGaB algorithm provides better error correction performance compared with GaB. GaB and IGaB algorithms are implemented on Field Programmable Gate Array (FPGA) to compare hardware performance.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: 2Citation - Scopus: 2Fine Tuning DeepSeek and Llama Large Language Models with LoRA(IEEE, 2025-06-25) Uluirmak, Bugra Alperen; Kurban, RifatIn this paper, Low-Rank Adaptation (LoRA) finetuning of two different large language models (DeepSeek R1 Distill 8B and Llama3.1 8B) was performed using the Turkish dataset. Training was performed on Google Colab using A100 40 GB GPU, while the testing phase was carried out on Runpod using L4 24 GB GPU. The 64.6 thousand row dataset was transformed into question-answer pairs from the fields of agriculture, education, law and sustainability. In the testing phase, 40 test questions were asked for each model via Ollama web UI and the results were supported with graphs and detailed tables. It was observed that the performance of the existing language models improved with the fine-tuning method.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: 6Citation - Scopus: 14Autonomous 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*.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.Conference Object Akciğer Tümörlü Hastaların PET ve BT Görüntülerinin Çakıştırılıp Birleştirilmesi(IEEE, 2015-10) Ayyildiz, Oguzhan; Yilmaz, Bulent; Karacavus, Seyhan; Kayaalti, Omer; Icer, Semra; Eset, Kubra; Kaya, EserImage fusion attracts attention in medical field due to complementary behavior and application such as diagnosis and treatment planning. In this study, first positron emission tomography (PET) and computed tomography (CT) images coming from 8 nonsmall cell lung cancer were registered then wavelet and principal component analysis methods were applied to fuse images. According to mutual information metric and nuclear medicine expert wavelet method gave better results when compared to PCA.