Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Start date: 2020Language: search.filters.language.tr

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Now showing 1 - 10 of 46
  • Conference Object
    Adapting Language Models to Sentiment Analysis for Automatically Translated and Labelled Turkish News Texts
    (Institute of Electrical and Electronics Engineers Inc., 2025) Serficeli, S.C.; Udunman, B.; Inan, E.
    The proliferation of news sources makes it difficult to track current events and social events in real time. In order to interpret social events in this context quickly and effectively, it is important to translate news texts provided in different natural languages into Turkish and to perform sentiment analysis on them. The aim of this study is to translate multilingual news texts into Turkish and perform sentiment analysis on these texts. The generated labels were compared and the data that were given the same label by all models were separated as automatically labelled data. This automatic labelling process ensured that the data for which different models produced consistent results were reliably labelled. When the results were evaluated, F1 score of 0.946 was achieved for sentiment analysis using the automatic labelling mechanism for texts translated into Turkish. © 2025 IEEE.
  • Conference Object
    Outage and Intercept Performance in THz LEO-Ground Communication With Satellite Selection
    (IEEE, 2025) Bakirci, Emre Berker; Ahrazoglu, Evla Safahan; Altunbas, Ibrahim; Erdogan, Eylem
    Satellite communication and THz communication systems are some of the methods that aim to meet the demand of increasing data rates. With an importance growing alongside increasing data amounts, data security is on its way to a position that cannot be neglected when building systems. In this study, it has been shown that secure data transmission can be made possible through the use of THz frequencies in a link between LEO satellites and a ground station. Proposed scenarios data transmission performance have been analyzed. It has been shown that selection transmission have improved both data transmission and security performances.
  • Conference Object
    Performance Evaluation of Filter-Based Gene Selection Methods in Cancer Classification
    (IEEE, 2025) Gokalp, Osman
    With the advances in microarray technology, gene expression levels can be measured efficiently, and this data can be used to solve important problems such as cancer classification. However, microarray data suffers from the high-dimensionality problem and requires dimensionality reduction techniques such as feature selection. This study addresses the cancer classification problem using microarray datasets and comparatively evaluates the performance of different filter-based gene (feature) selection methods. To this end, 11 microarray datasets have been evaluated using 6 different filter methods, and experimental results are presented. According to the findings, the gene selection methods used can improve classification performance by 5% to 30%. Using 5-fold cross-validation, the highest accuracy rates were achieved with 32 genes selected by the gain ratio filter for the Breast and Colon datasets, and with 8 genes selected by the information gain filter for the CNS dataset.
  • Conference Object
    Machine Learning-Based Antenna Selection and Secrecy Capacity Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2025) Erdurak, Burak; Erdoǧan, Eylem; Gürkan, Filiz
    The performance of machine learning methods was analyzed to optimize antenna selection in wireless communication systems, and system's secrecy performance was observed. To enhance the antenna selection process, Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and the KNearest Neighbors (KNN) algorithm were utilized. Channel vectors were used as model inputs, aiming to select the most optimal transmission path among N possible candidates. During the training phase, the antenna with the highest Signal-to-Noise Ratio (SNR) was selected for data labeling. The performance of Single-Input Multiple-Output (SIMO), Multiple-Input SingleOutput (MISO), and Multiple-Input Multiple-Output (MIMO) system architectures was evaluated using model accuracy and the F1-score. Additionally, the secrecy capacity corresponding to the selected antennas was computed, demonstrating the feasibility of secure communication. The results indicate that deep learningbased methods achieved higher accuracy, with the CNN model emerging as the most successful approach, reaching an accuracy of over 95% across all system configurations. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Kentlerin Depreme Dirençliliğinin Bina Bazlı Bölgesel Risk Dağılımı Yöntemi İle İncelenmesi: İzmit Kenti Örneği
    (Afet ve Acil Durum Yonetimi Baskanligi, 2024) Kurt, Deniz Gerçek; Guven, Ismaıl Talıh; Erdogan, Hakan
    Türkiye, yıkıcı deprem üretme potansiyeli yüksek fay hatlarının yer aldığı bir bölgede konumlanmaktadır. Tarih boyunca, Anadolu yarımadasında meydana gelen depremler büyük can ve mal kayıplarına sebep olmuştur. Bu bağlamda, nüfusun ve sanayileşmenin çok yoğun olduğu Marmara Bölgesinde deprem risk değerlendirme çalışmalarının yoğunlaşması ve gerekli önlemlerin alınması büyük önem arz etmektedir. Bu çalışmada, 6306 sayılı \"Afet Riski Altındaki Alanların Dönüştürülmesi Hakkında Kanun\" kapsamında tanımlanan binaların bölgesel deprem riski dağılımının belirlenmesi için riskli yapıların tespitine ilişkin esaslar başlığı altında öngörülen basitleştirilmiş yöntemler kullanılarak Kocaeli ili İzmit ilçesinde bulunan 19940 bina incelenmiştir. Söz konusu yöntem, hızlı sokak taraması prensiplerini dikkate alarak bölgesel deprem risk önceliklendirmesini hedeflemektedir. Saha incelemelerinden elde edilen sonuçlar, nüfus yoğunluğu ve dağılımı, acil toplanma alanı dağılımı, toplam yapı alanı gibi parametrelerle beraber değerlendirilerek İzmit ilçesinde deprem risk öncelikli bölgelerin belirlenmesine çalışılmıştır.
  • Article
    Citation - Scopus: 3
    University Librarians’ Perceptions of Artificial Intelligence, Its Application Areas in Libraries, and The Future
    (University and Research Librarians Association (UNAK), 2024) Gürdal, Gültekin; Çuhadar, Sami; Mert, Selma; Gezer, Çağatay; Helvacıoğlu, Ece; Arus, Oya; Aslan, Özlem; Karslı, Melahat; Sönmez, Çiğdem; Taş, Ali; Açıkalın, Cansu; Mazlumoğlu, Ayça Aydemir; Erken, Mehmet; Yılmaz, Müberra; Çerkez, Özlem Araz; Uğur, Emrullah; Menemenlioğlu, Alper; Şenoğlu, Aysel; Atlı, Songül; Cuhadar, Sami; Gurdal, Gultekin; Erken, Mehmet; Mert, Selma; Gezer, Cagatay; Helvacıoğlu, Ece; Atli, Songül
    Günümüzde kütüphaneler, değişen teknoloji ve yeniliklerden etkilenen kurumlar arasında yer almaktadır. Yapay zeka teknolojilerinin popüler hale gelmesi, kütüphane hizmetlerini de dönüştürmeye başlamıştır. Bu araştırmada, Türkiye’deki üniversite kütüphanelerinin yapay zeka teknoloji ve uygulamalarının gelişim sürecinde yapmış olduğu ve yapmayı planladığı düzenlemeleri tespit etmek ve ilgili döneme özel geliştirdikleri hizmetleri belirlemek amacıyla bir anket uygulanmıştır. Anket, Türkiye’deki 208 üniversite kütüphanesinden 111 üniversite kütüphanesi yöneticisinin katılımıyla gerçekleştirilmiştir. Verilerin analizi ile üniversite kütüphanelerinin yapay zeka teknolojileri ve uygulamaları hakkındaki durumu, bilgi ve farkındalık düzeyleri belirlenmiş, eksik ve zayıf yönlerin geliştirilmesine yönelik önlemler ve öneriler sunulmuştur. İlgili araştırma, yapay zeka konusunda Türkiye’de üniversite kütüphanesi yöneticilerinden görüş ve öneri alarak gerçekleştirilen ilk ve en kapsamlı çalışmadır. Araştırma bulguları, üniversite kütüphanelerinin ChatGPT, Gemini, Grammarly vb. yapay zeka uygulamalarını belirli düzeyde kullandıklarını ancak yapay zeka ile ilgili kurumsal politika geliştirme, personele yetkinlik kazandırma ve planlama konularında ihtiyaçlarının olduğu ortaya çıkmıştır.
  • Conference Object
    Citation - Scopus: 2
    Outage Probability Analysis of Triple-Hybrid Rf/Fso Communication System
    (Ieee, 2024) Bakirci, Emre Berker; Ahrazoglu, Evla Safahan; Altunbas, Ibrahim; Erdogan, Eylem
    Terahertz (THz) and free space optical (FSO) transmission techniques are considered as alternatives for radio frequency (RF) transmission to meet the requirements for the sixth generation and beyond wireless communication systems. However, these transmission techniques may experience high level of deterioration under different weather conditions. In this study, triple-hybrid RF/FSO/THz system is proposed to enhance the system performance. The results have shown that the proposed system has improved outage probability performance under wide range weather conditions compared to only RF, only FSO, and only THz systems as well as dual-hybrid systems (RF/FSO, RF/THz, and FSO/THz). Theoretical results are validated via computer simulations.
  • Conference Object
    Improvements on a Multi-Task Bert Model
    (Ieee, 2024) Agrali, Mahmut; Tekir, Selma
    Pre-trained language models have introduced significant performance boosts in natural language processing. Fine-tuning of these models using downstream tasks' supervised data further improves the acquired results. In the fine-tuning process, combining the learning of tasks is an effective approach. This paper proposes a multi-task learning framework based on BERT. To accomplish the tasks of sentiment analysis, paraphrase detection, and semantic text similarity, we include linear layers, a Siamese network with cosine similarity, and convolutional layers to the appropriate places in the architecture. We conducted an ablation study using Stanford Sentiment Treebank (SST), Quora, and SemEval STS datasets for each task to test the framework and its components' effectiveness. The results demonstrate that the proposed multi-task framework improves the performance of BERT. The best results obtained for sentiment analysis, paraphrase detection, and semantic text similarity are accuracies of 0.534 and 0.697 and a Pearson correlation coefficient of 0.345.
  • Conference Object
    Citation - WoS: 1
    Konteyner Görüntülerini Kullanarak Hasar Tespiti ve Sınıflandırması
    (IEEE, 2020) Imamoglu, Zeynep Ekici; Tuglular, Tugkan; Bastanlar, Yalin
    In the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including container damage detection. During container warehouse entry / exit process, the process of detecting damaged containers is carried out by the personnel and several minutes are required to upload to the IT system. The aim of our work is to automate the detection of damaged containers. This way, the mistakes made by the personnel will be eliminated and the process will be accelerated. In this work, we propose to use a convolutional neural network (CNN) that takes the container images and classify them as damaged or undamaged. We modeled the problem as a binary classification and employed different CNN models. The result we obtained shows that there is no single best method for the classification. It is shown how the dataset was created and how the parameters used in the layered structures affect the models employed in this study.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Görgül kip ayrıştırması kullanılarak optik faz kırınımında hassasiyet iyileştirilmesi
    (IEEE, 2023) Ataç, Enes; Dinleyici, Mehmet Salih
    Phase diffraction is a potent property used in transparent dielectric film characterization. The measured diffraction pattern on the camera is evaluated by matching numerically computed diffraction patterns to determine the optical properties of the ultra-thin films (refractive index, thickness, etc.). However, the obtained diffraction data is not only a nonlinear and non-stationary signal but also exhibits micron-scale variations, thus limiting the measurement accuracy. Therefore, it is challenging to identify shifts in minima and deviations in amplitude on diffraction data to extract information about the optical properties of phase objects. In this study, it is aimed to improve the thickness sensitivity of the system by applying Empirical Mode Decomposition (EMD) to plane wave-based near-field phase diffraction data. Since EMD is very sensitive to abrupt changes in the signal due to the spatial frequency components, the nanoscale variations in the film thickness become more observable and detectable. Experimental outputs and numerical simulations show that the decomposition increases the thickness sensitivity comparing the classical matching technique.