Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Generating Lost Urban Fabric: Exploration of Generative Adversarial Networks as a Design Tool in Post-Disaster Urban Recovery(Education and Research in Computer Aided Architectural Design in Europe, 2025) Takış, F.N.; Akyüz, S.This study investigates the use of GANs, particularly the Pix2PixHD, for reconstructing urban fabric and preserving urban memory in post-disaster contexts, focusing on Hatay, Türkiye, after the 2023 earthquakes. Models were trained on pre-disaster urban maps and tested on incomplete post-earthquake data to regenerate damaged urban areas. Evaluation metrics, including FID scores, SSIM values, and visual inspections, demonstrated the model's ability to produce contextually accurate designs. The trained model effectively maintained road networks, building geometries, and spatial coherence. In addition to spatial consistency, the model produced outputs with sharp edges and high visual clarity. These results highlight the significant potential of GANs as generative design tools, offering valuable support to urban planners and architects in balancing urgent reconstruction needs with the long-term preservation of urban identity and memory in disaster-affected areas. © 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.Article Citation - Scopus: 5University Librarians’ Perceptions Of Artificial Intelligence, Its Application Areas İn Libraries, And The Future(University and Research Librarians Association (UNAK), 2024-12-26) Cuhadar, S.; Mert, S.; Gezer, Ç.; Helvacioğlu, E.; Arus, O.; Aslan, Ö.; Atli, S.; Gurdal, Gultekin; Erken, MehmetToday, libraries are among the institutions affected by changing technology and innovations. The popularization of artificial intelligence (AI) technologies has also begun to transform library services. In this research, a survey was conducted to determine the adjustments that university libraries in Turkey have made and plan to make during the development process of AI technologies and applications, and to identify the services they have developed specific to the relevant period. The survey was carried out with the participation of 111 university library managers from 208 university libraries in Turkey. Through the analysis of the data, the status, knowledge, and awareness levels of university libraries regarding AI technologies and applications were determined, and measures and recommendations were presented to improve deficiencies and weaknesses. This research is the first and most comprehensive study conducted in Turkey by obtaining opinions and suggestions from university library managers on artificial intelligence. The research findings revealed that university libraries use AI applications such as ChatGPT, Gemini, and Grammarly to a certain extent; however, they have needs in developing institutional policies, enhancing personnel competencies, and planning related to AI. © 2024 University and Research Librarians Association (UNAK). All rights reserved.Article Citation - WoS: 1Comprehensive Prediction of FBN1 Targeting Mirnas: A Systems Biology Approach for Marfan Syndrome(Galenos Publishing House, 2025-09-22) Orhan, M.E.; Demirci, Y.M.; Saçar Demirci, M.D.S.; Demirci, Muserref Duygu SacarObjective: Marfan syndrome (MFS) is a genetic connective tissue disorder primarily caused by mutations in the FBN1 gene. Emerging evidence highlights the regulatory role of microRNAs (miRNAs) in modulating gene expression in MFS, but a systematic investigation into miRNAs targeting FBN1 is lacking. This study aimed to comprehensively identify miRNAs interacting with the FBN1 transcript to reveal potential molecular regulators and therapeutic targets. Methods: Human miRNA sequences were retrieved from miRBase (Release 22.1), and the canonical FBN1 transcript (RefSeq: NM_000138.5) was used for target prediction. Computational interaction analysis was conducted using the psRNATarget server with stringent parameters to detect potential miRNA binding sites. Expression profiles and disease associations of the top candidate miRNAs were further investigated through database integration and literature review. Results: Out of 2656 human mature miRNAs analyzed, 251 were predicted to bind FBN1, with the hsa-miR-181 family exhibiting the highest number of predicted interactions. Evidence from the literature highlighted dysregulation of hsa-miR-181 expression in MFS patients, suggesting a functional role in disease pathophysiology. Conclusion: This study identifies key members of the hsa-miR-181 family as post-transcriptional regulators of FBN1, offering new insights into miRNA-driven mechanisms in MFS. These findings support the potential of RNA-based diagnostics and therapeutic strategies targeting miRNA-FBN1 interactions. ©Copyright 2025 The Author.Conference Object TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling(Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics is an innovative text classification framework that integrates topic modeling with feature selection to improve model accuracy and interpretability. Unlike traditional methods that rely on individual words, TextNetTopics selects cohesive topics extracted via Latent Dirichlet Allocation as features for document representation, effectively reducing dimensionality while preserving the semantic structure of the text. This study evaluates the performance of TextNetTopics utilizing multiple machine learning algorithms in the M (Modeling) component, including Random Forest, Support Vector Machine, Gradient Boosting, eXtreme Gradient Boosting, and Logistic Regression. To further enhance classification performance, we introduce TextNetTopics+, an ensemblebased extension that leverages both hard voting and soft voting mechanisms to combine the strengths of multiple classifiers. Comprehensive experiments on the LitCovid and WOS datasets demonstrate that ensemble learning in TextNetTopics + significantly outperforms individual classifiers in TextNetTopics, confirming its effectiveness in improving model robustness and generalization.Conference Object Leveraging MicroRNA-Gene Associations With Mirgedinet: An Intelligent Approach for Enhanced Classification of Breast Cancer Molecular Subtypes(Springer International Publishing AG, 2025) Qumsiyeh, Emma; Bakir-Gungor, Burcu; Yousef, MalikUnderstanding the molecular subtypes of breast cancer is crucial for advancing targeted therapies and precision medicine. For the BRCA molecular subtype prediction problem, this study employs miRGediNET, a machinelearning approach that integrates data from miRTarBase, DisGeNET, and HMDD databases to investigate shared gene associations between microRNA (miRNA) activity and disease mechanisms. Using the BRCA LumAB_Her2Basal dataset, we evaluate miRGediNET's performance against traditional feature selection methods, including CMIM, mRmR, Information Gain (IG), SelectKBest (SKB), Fast Correlation-Based Filter (FCBF), and XGBoost (XGB). These feature selection techniques were assessed using various classification algorithms including Random Forest (RF), Support Vector Machine (SVM), LogitBoost, Decision Tree, and AdaBoost, all executed with default parameters. The feature selection methods were tested using Monte Carlo Cross-Validation, where performance metrics obtained for each iteration were averaged to ensure robustness. Our findings reveal that miRGediNET outperforms traditional methods in accuracy and Area Under the Curve (AUC), emphasizing its superior capability to identify key genes that bridge miRNA interactions and breast cancer mechanisms. Notably, both miRGediNET and Information Gain (IG) feature selection consistently identified ESR1, a critical biomarker frequently reported in recent research associated with breast cancer prognosis and resistance to endocrine therapies. This integrative approach provides deeper biological insights into miRNA-disease interactions, paving the way for enhanced patient stratification, biomarker discovery, and personalized medicine strategies. The miRGediNET tool, developed on the KNIME platform, offers a practical resource for further exploration in the field of bioinformatics and oncology.Article An Opportunity for Nuri Demirağ the National Development Party the Transition to Multiparty Life in Turkey(Osman Kose, 2023) Karatas, Murat; Solak, YeterThe political life in Turkey during the period of 1923-1945 showed a single-party characteristic. Turkey, which did not enter the Second World War in 1939-1945, faced some negativities brought by the war both during and after the war. These negativities have had a compelling effect on the government to switch to a multi-party system with the effect of external and internal factors. Nuri Demira & gbreve; founded the National Development Party while he was preparing to take a step towards a multiparty system in Turkey, forced by both domestic and foreign conditions. Although the party could not show an active presence in our political life, its establishment made an important contribution to the establishment of democracy culture and the Despite all the efforts of Nuri Demira & gbreve; as the founder and chairman of the party, the inconsistency in the party and the evaluation of party activities from the wrong points led to the party's indifference in political life. In 1957, the existence of the National Development Party in our political life came to an end quietly due to the fact that the party committee could not convene after the death of the party chairman, Nuri Demira & gbreve;.In this sense, our study aims to contribute to the literature by aiming to discuss the political adventure of the National Development Party, which stands at an important point in terms of the democratization process in our political life, and to reveal the role of its founder, Nuri Demirag, in this process.