Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Machine Learning-Based Antenna Selection and Secrecy Capacity Analysis(Institute of Electrical and Electronics Engineers Inc., 2025) Erdurak, Burak; Erdoǧan, Eylem; Gürkan, FilizThe 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.Conference Object Citation - Scopus: 1Observed Performance of a RC Wall-Frame Building During the February 2023 Turkey Earthquake and Performance Improvement Using FRPs(International Institute for FRP in Construction (IIFC), 2023) Tura, C.; Sahinkaya, Y.; Güllü, M.F.; Demir, U.; Orakcal, K.; Ilki, A.In this study, results of nonlinear response history analysis are presented for an existing RC wall-frame building, which has suffered collapse-level damage during the devastating February 2023 Kahramanmaras earthquakes. Performance analysis results for two building configurations are compared; first for the existing building configuration generated upon on-site observations, and second, for a hypothetical configuration in which the structural walls and columns are retroffited using externally-bonded FRP sheets. Analysis results reveal that in its existing configuration, mostly due to detailing deficiencies, a collapse-level performance was not unexpected; whereas FRP strengthening of the building would have resulted in collapse-prevention performance. © CICE 2023 - 11th International Conference on FRP Composites in Civil Engineering. All rights reserved.Conference Object Quote Detection: a New Task and Dataset for Nlp(Association for Computational Linguistics, 2023) Tekir, S.; Güzel, A.; Tenekeci, S.; Haman, B.U.Quotes are universally appealing. Humans recognize good quotes and save them for later reference. However, it may pose a challenge for machines. In this work, we build a new corpus of quotes and propose a new task, quote detection, as a type of span detection. We retrieve the quote set from Goodreads and collect the spans through a custom search on the Gutenberg Book Corpus. We run two types of baselines for quote detection: Conditional random field (CRF) and summarization with pointer-generator networks and Bidirectional and Auto-Regressive Transformers (BART). The results show that the neural sequence-to-sequence models perform substantially better than CRF. From the viewpoint of neural extractive summarization, quote detection seems easier than news summarization. Moreover, model fine-tuning on our corpus and the Cornell Movie-Quotes Corpus introduces incremental performance boosts. Finally, we provide a qualitative analysis to gain insight into the performance. © 2023 Association for Computational Linguistics.