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Bayrak, Merve

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Profile URL
https://hdl.handle.net/20.500.14901/1582
Name Variants
Merve Bayrak
Bayrak
Bayrak M
Bayrak M.
M. Bayrak
Bayrak, M
Bayrak, M.
Job Title
Araş. Gör.
Email Address
merve.bayrak@erzurum.edu.tr
Main Affiliation
4.4. Bilgisayar Mühendisliği Bölümü
Status
Website
https://erzurum.edu.tr/personel/4715/merve-bayrak#gsc.tab=0
ORCID ID
0000-0003-4320-6669
Scopus Author ID
58848209300
Turkish CoHE Profile ID
319282
Google Scholar ID
nsgxtaQAAAAJ
WoS Researcher ID
KCL-6139-2024

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  • Thumbnail Image
    Article
    A New Methodology for Automatic Creation of Concept Maps of Turkish Texts
    (Springer, 2025) Bayrak, Merve; Dal, Deniz
    Concept maps are two-dimensional visual tools that describe the relationships between concepts belonging to a particular subject. The manual creation of these maps entails problems such as requiring expertise in the relevant field, minimizing visual complexity, and integrating maps, especially in terms of text-intensive documents. In order to overcome these problems, automatic creation of concept maps is required. On the other hand, the production of a fully automated and human-hand quality concept map from a document has not yet been achieved satisfactorily. Motivated by this observation, this study aims to develop a new methodology for automatic creation of the concept maps from Turkish text documents for the first time in the literature. In this respect, within the scope of this study, a new heuristic algorithm has been developed using the Turkish Natural Language Processing software chain and the Graphviz tool to automatically extract concept maps from Turkish texts. The proposed algorithm works with the principle of obtaining concepts based on the dependencies of Turkish words in sentences. The algorithm also determines the sentences to be added to the concept map with a new sentence scoring mechanism. The developed algorithm has been applied on a total of 20 data sets in the fields of Turkish Literature, Geography, Science, and Computer Sciences. The effectiveness of the algorithm has been analyzed with three different performance evaluation criteria, namely precision, recall and F-score. The findings have revealed that the proposed algorithm is quite effective in Turkish texts containing concepts. It has also been observed that the sentence selection algorithm produces results close to the average value in terms of the performance criteria being evaluated. According to the findings, the concept maps automatically obtained by the proposed algorithm are quite similar to the concept maps extracted manually. On the other hand, there is a limitation of the developed algorithm since it is dependent on a natural language processing tool and therefore requires manual intervention in some cases.
  • Thumbnail Image
    Article
    Patchbridgenet: A Patch-Based Deep Feature Extraction and Classification Model for Automated Retinal Disease Diagnosis from Oct Images
    (Elsevier Sci Ltd, 2026) Bayrak, Merve; Dal, Deniz; Baygin, Mehmet
    Artificial intelligence plays a significant role in transforming diagnostic and treatment processes in the healthcare sector, enabling early disease detection and personalized treatment approaches. Optical Coherence Tomography (OCT) is one of the most essential tools in this transformation, allowing for the detailed examination and classification of retinal diseases. In this study, we developed an innovative Deep Feature Engineering (DFE) model named PatchBridgeNet for automated classification of retinal diseases using OCT images. The proposed PatchBridgeNet integrates the lightweight efficiency of MobileNetV2, the hierarchical feature extraction capabilities of DarkNet53 and the dense connectivity patterns of DenseNet201 to comprehensively capture both global context and local patch-level features. Features extracted through the patch-based approach were optimized using the Iterative Neighborhood Component Analysis (INCA) algorithm and the Chi-Square (Chi2) statistical method. The optimized features were then classified using Support Vector Machines (SVM). The model achieved an accuracy of 92.3 % in multi-class classification and 97.4 % in binary classification tasks. PatchBridgeNet enabled the effective analysis of both global and regional details in the diagnosis of retinal diseases, providing a significant advantage over existing methods. Furthermore, the patch-based structure of the model facilitated the capture of small but critical information in OCT images. The results demonstrate that PatchBridgeNet holds significant potential for OCT image analysis and other medical imaging applications.