Bildiri Koleksiyonu | Bilgisayar Mühendisliği Bölümü

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https://hdl.handle.net/11729/4898

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Saliency detection with hybrid artificial bee colony-firefly optimization method
(ICCESEN, 2018-12-28) Yelmenoğlu, Elif Deniz; Çelebi, Numan; Taşçı, Tuğrul; Akkurt, İskender; Günoğlu, Kadir; Akyıldırım, Hakan
Implementation of optimization algorithms in image processing is a quite common area of research. Detecting salient fields in images can be used for problems such as object recognition, image segmentation or video tracking problems. This case makes the determination of saliency an important factor in image processing. The algorithms developed for salient region detection are divided into two approaches as bottom-up and top-down. The bottom-up techniques determine salient regions according to the data, and the top-down techniques discover these regions by the learning of visual information of a certain object. This paper presents an optimization technique for bottom-up saliency detection algorithm based on Hybrid Artificial Bee Colony- Firefly algorithm.
TurkEmbed: Turkish embedding model on natural language inference & sentence text similarity tasks
(Institute of Electrical and Electronics Engineers Inc., 2025) Ezerceli, Özay; Gümüşçekiçci, Gizem; Erkoç, Tuğba; Özenç, Berke
This paper introduces TurkEmbed, a novel Turkish language embedding model designed to outperform existing models, particularly in Natural Language Inference (NLI) and Semantic Textual Similarity (STS) tasks. Current Turkish embedding models often rely on machine-translated datasets, potentially limiting their accuracy and semantic understanding. TurkEmbed utilizes a combination of diverse datasets and advanced training techniques, including matryoshka representation learning, to achieve more robust and accurate embeddings. This approach enables the model to adapt to various resource-constrained environments, offering faster encoding capabilities. Our evaluation on the Turkish STS-b-TR dataset, using Pearson and Spearman correlation metrics, demonstrates significant improvements in semantic similarity tasks. Furthermore, TurkEmbed surpasses the current state-of-the-art model, Emrecan, on All-NLI-TR and STS-b-TR benchmarks, achieving a 1-4% improvement. TurkEmbed promises to enhance the Turkish NLP ecosystem by providing a more nuanced understanding of language and facilitating advancements in downstream applications.
A metric-driven IT risk scoring framework: incorporating contextual and organizational factors
(Institute of Electrical and Electronics Engineers Inc., 2025-09-24) Ünal, Nezih Mahmut; Çeliktaş, Barış
Risk analysis is a critical process for organizations seeking to manage their cybersecurity posture effectively. However, traditional risk analysis frameworks, such as the Common Vulnerability Scoring System (CVSS), primarily evaluate technical impacts without incorporating organizational context and dynamic risk factors. This paper presents a metric-based risk analysis framework designed to provide a more adaptable and context-aware risk-scoring framework. The proposed model enables risk owners to define customized threat scenarios and dynamically adjust metric weights based on organizational needs. Unlike traditional approaches, our method integrates contextual parameters to improve the accuracy and relevance of risk calculations. Experimental evaluations demonstrate that the proposed framework enhances risk prioritization and provides more actionable insights for decision-makers. This study contributes to the field by addressing the limitations of existing risk analysis models and offering a systematic approach for cybersecurity risk management.
LLM’leri kullanarak otel incelemelerini görüntü manipülasyonu ile görselleştirme
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta Said
Dijital çağda müşteri yorumları, özellikle otelcilik sektöründe, karar verme sürecinde önemli bir rol oynamaktadır. Metin tabanlı yorumlar değerli bilgiler sunsa da, potansiyel müşteriler genellikle öznel ifadeleri doğru şekilde yorumlamakta zorlanmaktadır. Araştırmalar, görsel temsillerin anlaşılırlığı artırdığını ve kullanıcı etkileşimini güçlendirdiğini göstermektedir. Bu çalışma, metin tabanlı görüntü manipülasyonu ile yazılı otel yorumlarını orijinal otel görselleri üzerinde değişiklikler yaparak görsel incelemelere dönüştürmeyi amaçlamaktadır. Stable Diffusion modeli kullanılarak yazılı otel yorumları girdileriyle otel odası görüntüleri manipüle edilmiştir. Manipüle edilen görsellerin değerlendirilmesinde SSIM (Structural Similarity Index Measure) ve PSNR (Peak Signal-to-Noise Ratio) metrikleri uygulanmıştır. Ayrıca, manipüle edilmiş ve orijinal görüntü örnekleri karşılaştırmalı olarak sunulmuştur. Sonuçlar, modelin küçük ölçekli değişikliklerde başarılı olduğunu, ancak büyük değişikliklerde kalite kaybı yaşadığını göstermektedir.
Mahremiyeti koruyan, merkezi, hibrit film öneri sistemi: araçlar arası internet için bir yaklaşım
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Şimşek, Musa; Tüysüz Erman, Ayşegül
Bu çalışmada, kullanıcı verilerinin gizliliğini korurken öneri doğrulu günü artırmayı hedefleyen, diferansiyel mahremiyet destekli hibrit bir öneri modeli sunulmuştur. Model mimarisi, Matris Çarpanlaması (MF), Çok Katmanlı Algılayıcı (MLP) ve Uzun Kısa Süreli Bellek (LSTM) ağlarını birleştirmektedir. Laplace mekanizmasına dayalı gürültü enjeksiyonu ile eğitim sürecinde diferansiyel mahremiyet sağlanmış ve ayrıca hiperparametre optimizasyonu uygulanmıştır. Model, kullanıcı film etkileşimlerini içeren MovieLens 100K veri kümesi üzerinde değerlendirilmiştir. Performans değerlendirmesi MSE, MAE ve NDCG metrikleriyle yapılmış; hiperparametre optimizasyonu ile MSE bazında yaklaşık %4 iyileşme sağlandığı, yüksek gizlilik düzeyinde ise doğrulukta yaklaşık %39 oranında bozulma yaşandığı gözlemlenmiştir.
TurkEmbed4Retrieval: Türkçe için geri getirme görevine özel gömme modeli
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Ezerceli, Özay; Gümüşçekiçci, Gizem; Erkoç, Tuğba; Özenç, Berke
Bu çalışmada, öncelikle Doğal Dil Çıkarımı (DDÇ) ve Anlamsal Metin Benzerliği (AMB) görevleri için geliştirilen TurkEmbed modelinin, MS-Marco-TR veri seti üzerinde ince ayar yapılarak geri getirme görevlerine uygun hale getirilmesini sağlayan TurkEmbed4Retrieval modelini tanıtıyoruz. Model, Matruşka temsili ögrenme ve özel tasarlanmış negatif çiftlerin sıralanması kayıp fonksiyonu gibi ileri seviye egitim teknikleri kullanılarak optimize edilmiştir. Yapılan kapsamlı deneyler, TurkEmbed4Retrieval’ın, geri getirme metriklerinde TurkishcolBERT modelini Scifact-TR veri kümesinde %19–26 oranında geçtiğini göstermektedir. Bu bağlamda, modelimiz, Türkçe bilgi getirme sistemleri için yeni bir çıtaya ulaşmaktadır.
Comparing pre-trained and fine-tuned transformer-based models for sentiment analysis in Turkish comments in student surveys
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Pourjalil, Kajal; Ekin, Emine; Recal, Füsun
Student surveys are essential for evaluating teaching quality and course content, but analyzing open-ended responses is challenging due to their unstructured and multilingual nature. This study applies sentiment analysis to Turkish educational survey responses using three transformer-based models: SAVASY, DBMDZ BERT Base Turkish Cased, and XLM-RoBERTa Base. A labeled dataset of real-world student comments was used, with sentiment labels assigned using the Gemini AI tool to facilitate model fine-tuning. Evaluation metrics included accuracy, F1-score, precision, recall, and confidence scores. Results show that fine-tuning improves sentiment classification, effectively identifying positive, negative, and neutral sentiments. This highlights the value of transformer models in analyzing Turkish student feedback.
Çok ölçekli görsel benzerlik analizi ile oltalama saldırısı tespiti
(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Kılıç, Bartu; Çeliktaş, Barış
Oltalama saldırıları teknolojinin gelişmesiyle günümüzün en yaygın siber güvenlik tehditlerinden biri haline gelmiştir. Bu çalışma, web sitelerinin ekran görüntülerini gelişmiş bir görsel benzerlik analizi yöntemiyle inceleyerek oltalama saldırılarını yüksek doğrulukla tespit eden bir yaklaşım sunmaktadır. Oltalama tespiti için önerilen yöntemde, algısal özütleme tabanlı çoklu çözünürlük analizi, akıllı ilgi bölgesi (ROI) tespiti ve çoklu metrik füzyonu gibi teknikler birleştirilerek yüksek doğrulukta tespit yapılabilmektedir. Veri seti, popüler bankacılık, e-posta ve sosyal medya platformlarının gerçek ve oltalama sayfalarından oluşan 23 gerçek ve 3 oltalama sayfası ekran görüntülerinden derlenmiştir. Yapılan testler, yöntemin %85 doğruluk oranı ile tekil metrik tabanlı yaklaşımlardan daha iyi performans gösterdiğini ortaya koymuştur. Dil bağımsız çalışan bu yöntem, URL ve HTML manipülasyonlarına karşı dayanıklıdır ve gerçek zamanlı oltalama tespiti için güçlü bir çözüm sunmaktadır.
A context-aware, AI-driven load balancing framework for incident escalation in SOCs
(Institute of Electrical and Electronics Engineers Inc., 2025-08-12) Abuaziz, Ahmed; Çeliktaş, Barış
SOCs face growing challenges in incident management due to increasing alert volumes and the complexity of cyberattacks. Traditional rule-based escalation models often fail to account for the workload of the analyst, the severity of the incident, and the organizational context. This paper proposes a context-aware, AI-driven load balancing framework for intelligent analyst assignment and incident escalation. Our framework leverages large language models (LLMs) with retrievalaugmented generation (RAG) to evaluate incident relevance and historical assignments. A reinforcement learning (RL)-based scheduler continuously optimizes incident-to-analyst assignments based on operational outcomes, enabling the system to adapt to evolving threat landscapes and organizational structures. Planned simulations in realistic SOC environments will compare the model with traditional rule-based models using metrics such as Mean Time to Resolution (MTTR), workload distribution, and escalation accuracy. This work highlights the potential of AIdriven approaches to improve SOC performance and enhance incident response effectiveness.
Real-time 3D inspection of large civil structures using a stereoscopic camera system equipped UAV
(International Society for Photogrammetry and Remote Sensing, 2024-12-13) Akça, Devrim; Torkut, Çağın; Kemper, Gerhard; Kunz, Noah; Caner, Doğa; Gruen, Armin
REALTIME3D is an innovative Mixed-Reality (MR) photogrammetry system that integrates photogrammetry, UAV technology, and VR/AR solutions to enable real-time 3D infrastructure inspection. By combining these technologies, the system allows users to remotely access, visualize, and measure 3D stereoscopic models in real time. An operator on-site pilots a UAV equipped with the stereo cameras, while experts, utilizing VR headsets, can observe and analyze the object of interest from remote locations. This approach enhances cost efficiency and safety during inspections of large-scale, critical structures. The paper introduces the first prototype of the system, detailing its hardware and software components.
Efficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2024) Harb, Mhd Raja Abou; Çeliktaş, Barış
This paper presents a novel approach to estimating Sigmoid and Tanh activation functions using Artificial Neural Networks (ANN) optimized for homomorphic encryption. The proposed method is compared against second-degree polynomial and Piecewise Linear approximations, demonstrating a minor loss in accuracy while maintaining computational efficiency. Our results suggest that the ANN-based estimator is a viable alternative for secure machine learning models requiring privacypreserving computation.
Multi-task learning on mental disorder detection, sentiment analysis, and emotion detection using social media posts
(Institute of Electrical and Electronics Engineers Inc., 2024) Armah, Courage; Dehkharghani, Rahim
Mental disorders such as suicidal behavior, bipolar disorder, depressive disorders, and anxiety have been diagnosed among the youth recently. Social media platforms such as Reddit have become popular for anonymous posts. People are far more likely to share on these social media platforms what they really feel like in their real lives when they are anonymous. It is thus helpful to extract people's sentiments and feelings from these platforms in training models for mental disorder detection. This study uses multi-task learning techniques to examine the estimation of behaviors and mental states for early mental disease diagnosis. We propose a multi-task system trained on three related tasks: mental disorder detection as the primary task, emotion analysis, and sentiment analysis as auxiliary tasks. We took the SWMH dataset, which included four main different mental disorders already labeled (bipolar, depression, anxiety, and suicide) and offmychest. We then added labels for emotion and sentiment to the dataset. The observed results are comparable to previous studies in the field and demonstrate that deep learning multi-task frameworks can improve the accuracy of related text classification tasks when compared to training them separately as single-task systems.
Sentiment analysis for hotel reviews in Turkish by using LLMs
(Institute of Electrical and Electronics Engineers Inc., 2024) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta Said
The field of sentiment analysis plays a pivotal role in consumer decision-making and service quality improvement within the hospitality industry. This study explores the application of Large Language Models (LLMs) for sentiment analysis of Turkish hotel reviews, contributing to the understanding of customer feedback and satisfaction. We created a dataset of 5,000 reviews by translating an English corpus into Turkish, which was then utilized to evaluate the performance of a state-of-the-art Turkish language model, TURNA. The study demonstrates that LLMs, particularly TURNA, outperform traditional machine learning algorithms and other advanced models in sentiment classification tasks, achieving an accuracy of 99.4%. This research underscores the potential of LLMs to enhance the accuracy of sentiment analysis, offering valuable insights for the tourism and hospitality sectors. The findings contribute to the ongoing evolution of sentiment analysis methodologies and suggest that LLMs can significantly improve t he understanding a nd processing of customer feedback in Turkish hotel reviews.
Retinal disease classification using optical coherence tomography angiography images
(Institute of Electrical and Electronics Engineers Inc., 2024) Aydın, Ömer Faruk; Nazlı, Muhammet Serdar; Tek, Faik Boray; Turkan, Yasemin
Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging modality widely used for the detailed visualization of retinal microvasculature, which is crucial for diagnosing and monitoring various retinal diseases. However, manual interpretation of OCTA images is labor-intensive and prone to variability, highlighting the need for automated classification methods. This study presents an aproach that utilizes transfer learning to classify OCTA images into different retinal disease categories, including age-related macular degeneration (AMD) and diapethic retinopathy (DR). We used the OCTA-500 dataset [1], the largest publicly available retinal dataset that contains images from 500 subjects with diverse retinal conditions. To address the class imbalance, we employed k-fold cross-validation and grouped various other conditions under the 'OTHERS' class. Additionally, we compared the performance of the ResNet50 model with OCTA inputs to that of the ResNet50 and RetFound (Vision Transformer) models with OCT inputs to assess the efficiency of OCTA in retinal condition classification. In the three-class (AMD, D R, Normal) classification, ResNet50-OCTA o utperformed ResNet50-OCT, but slightly underperformed compared to RetFound-OCT, which was pretrained on a large OCT dataset. In the four-class (AMD, DR, Normal, Others) classification, ResNet50-OCTA and RetFound-OCT achieved similar classification a ccuracies. This study establishes a baseline for retinal condition classification using the OCTA-500 dataset and provides a comparison between OCT and OCTA input modalities.
Segmentation based classification of retinal diseases in OCT images
(Institute of Electrical and Electronics Engineers Inc., 2024) Eren, Öykü; Tek, Faik Boray; Turkan, Yasemin
Volumetric optical coherence tomography (OCT) scans offer detailed visualization of the retinal layers, where any deformation can indicate potential abnormalities. This study introduced a method for classifying ocular diseases in OCT images through transfer learning. Applying transfer learning from natural images to Optical Coherence Tomography (OCT) scans present challenges, particularly when target domain examples are limited. Our approach aimed to enhance OCT-based retinal disease classification by leveraging transfer learning more effectively. We hypothesize that providing an explicit layer structure can improve classification accuracy. Using the OCTA-500 dataset, we explored various configurations by segmenting the retinal layers and integrating these segmentations with OCT scans. By combining horizontal and vertical cross-sectional middle slices and their blendings with segmentation outputs, we achieved a classification a ccuracy of 91.47% and an Area Under the Curve (AUC) of 0.96, significantly outperforming the classification of OCT slice images.
Integrating the focusing neuron model with N-BEATS and N-HiTS
(Institute of Electrical and Electronics Engineers Inc., 2024) Özçelik, Şuayb Talha; Tek, Faik Boray
The N-BEATS (Neural Basis Expansion Analysis for Time Series) model is a robust deep learning architecture designed specifically for time series forecasting. Its foundational idea lies in the use of a generic, interpretable architecture that leverages backward and forward residual links to predict time series data effectively. N - BEATS influenced the development of N-HiTS (Neural Hierarchical Interpretable Time Series), which builds upon and extends the foundational ideas of N-BEATS. This paper introduces new integrations to enhance these models using the Focusing Neuron model in blocks of N-BEATS and N-HiTS instead of Fully Connected (Dense) Neurons. The integration aims to improve the forward and backward forecasting processes in the blocks by facilitating the learning of parametric local receptive fields. Preliminary results indicate that this new usage can significantly improve model performances on datasets that have longer sequences, providing a promising direction for future advancements in N-BEATS and N-HiTS.
Implementing lightweight, dynamic hierarchical key assignment scheme for cloud computing
(IEEE, 2024-03-25) Çelikbilek, İbrahim; Çeliktaş, Barış; Özdemir, Enver
In this paper, we propose the implementation and adaptation of a hierarchical key assignment scheme (HKAS) previously developed in our research to improve access control in cloud computing environments. The secret keys generated and managed by this scheme can be utilized for various purposes within the cloud computing, including data encryption, integrity checks, secure communications, and accessing critical infrastructures or services. Our implementation performs dynamic update operations with minimal computational cost and storage demands, as users within the hierarchical structure do not store any key components. Through security analysis, the scheme demonstrates strong key indistinguishability security (S-KI-security), effectively safeguarding keys against various cryptographic attacks. The scheme's flexibility allows it to be tailored to specific organizational needs, whether for securing sensitive data, ensuring compliance with regulatory standards, or facilitating secure data sharing and collaboration in cloud environments. Thus, we advocate for the practical implementation of the HKAS in transitioning to cloud environments.
Leveraging transformer-based language models for enhanced service insight in tourism
(IEEE, 2023-12-22) Er, Aleyna; Özçelik, Şuayb Talha; Yöndem, Meltem Turhan
Customer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning.
Forecasting and analysis of energy consumption and waste generation in Antalya with SVR
(IEEE, 2023-12-24) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, Erdal
Antalya, a rapidly expanding coastal city in Türkiye, has experienced significant changes due to urbanization and increasing tourism activities. Comprehending tourism trends is crucial for the city's sustainable development and environmental management. Based on this perspective, this paper aims to present a comprehensive retrospective analysis of Antalya's energy consumption, domestic solid waste generation, wastewater generation, population growth, and tourist numbers over the years. Antalya faces significant challenges due to escalating trends in listed areas. Utilizing the Support Vector Regression, this study projects a need for an additional 1715 GWh of electricity production capacity, an expansion of wastewater capacity by 85639 thousand m3, and an increase in domestic solid waste disposal capacity by 597745 tons by 2028 to accommodate growing demands. We emphasize the importance of adopting effective policies and strategies to support energy efficiency, waste reduction, and wastewater management alongside sustainable urban planning and tourism management for Antalya's long-Term environmental sustainability and development. The findings presented in this study provide valuable insights for policymakers, urban planners, and stakeholders to make informed decisions, ensuring a balanced approach toward economic growth and environmental conservation.
Application of ChatGPT in the tourism domain: potential structures and challenges
(IEEE, 2023-12-23) Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem Turhan
The tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.

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