FSM Vakıf Üniversitesi Araştırma ve Akademik Performans Sistemi
DSpace@FSM, FSM Vakıf Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
Güncel Gönderiler
Öğe Türü: Öğe , Bridging Engineering and Neuro-Oncology: A Scalable FastAPI-Deployed CNN Framework for Real-Time Explainable Brain Tumor Diagnosis(Frontiers in Neuroscience, 2026) Nematzadeh, Sajjad; Anka, Ferzat; Çiftçi, Fatih; Ayanoğlu, Kadriye Yasemin Usta; Özarslan, Ali Can; Öncü, EmirBackground: Automated and interpretable classification of brain tumors from MRI scans remains a critical challenge in medical imaging and neuro-oncology. This study addresses the need for reliable and deployable AI-driven tools that support timely tumor differentiation while maintaining transparency and practical usability. Methods: A deep learning–based diagnostic framework was developed using convolutional neural networks implemented in TensorFlow. The system was trained and evaluated on a curated dataset of 3,097 axial brain MRI images spanning four classes: glioma, meningioma, pituitary tumor, and normal cases. To ensure robust performance estimation, all models were evaluated using stratified 5-fold cross-validation and benchmarked against multiple state-of-the-art transfer learning architectures. For real-world applicability, the selected models were deployed via a FastAPI-based server, and Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated to provide qualitative visual explanations. Results: Across cross-validation folds, the proposed framework demonstrated stable and competitive performance in terms of accuracy, macro-averaged F1-score, and macro-averaged AUC, with low inter-fold variance. Comparative evaluation showed that transfer learning models achieved strong classification performance, while the lightweight custom CNN remained suitable for real-time deployment. The FastAPI implementation enabled low-latency inference and ondemand Grad-CAM visualizations, supporting transparent and responsive model usage. Conclusion: This work demonstrates the feasibility of bridging deep learning– based brain tumor classification with scalable, real-time deployment. By combining robust cross-validation, state-of-the-art benchmarking, and explainability- aware inference, the proposed framework provides a practical pathway toward integrating artificial intelligence into radiological workflows, while highlighting the importance of interpretability and deployment constraints in neuro-oncological applications.Öğe Türü: Öğe , A Large-Scale Peripheral Blood Cell Dataset for Automated Hematological Analysis(Nature, 2026) Yarıkan, Atıf Eren; Örer, Can; Akyıldız, Volkan; Kuş, Zeki; Aydın, Musa; Palaoğlu, Kerim Erhan; İncir, Said; Baysal, Kemal; Özçelik, Cemal; Kiraz, Berna; Kiraz, AlperWhite blood cell classification is fundamental to hematological diagnosis, yet existing datasets are limited in scale and class diversity. We present a comprehensive peripheral blood cell dataset comprising 31,489 high-resolution microscopic images across 13 distinct cell classes, representing the largest publicly available collection for automated blood cell analysis. Images are acquired using the Sysmex DI-60 system from May-Grünwald-Giemsa-stained blood smears at 100 × magnification under standardized laboratory conditions. Expert hematologists with over 10 years of experience performed manual annotation with high inter-rater agreement (Cohen’s kappa >0.85 for all classes). The dataset includes common cell types such as segmented neutrophils and lymphocytes, alongside diagnostically critical but rare subtypes, including myelocytes, blasts, and reactive lymphocytes. Images are organized into training, validation, and test splits (70:10:20 ratio) with consistent 368 × 368 pixel resolution. Baseline experiments using 14 deep learning architectures demonstrate the dataset’s utility, with DenseNet-121 achieving 95.23% accuracy. KU-Optofil PBC Dataset addresses critical gaps in medical image analysis datasets and supports the development of robust automated hematology systems for clinical applications.Öğe Türü: Öğe , Kütüphane Bülteni, 13(FSM Vakıf Üniversitesi, 2026)Sahn-ı Seman Medreseleri, Osmanlı İmparatorluğu’nun “Klasik Çağı'nı inşa eden insan kaynağının kalbidir. Bizans kiliselerindeki geçici çözümlerin ardından devletin kendi özgüveniyle ve muazzam bir bütçeyle kurduğu bu akademi, Fatih Sultan Mehmed’in “kılıçla fethedilen coğrafyanın ancak kalemle, kanunla ve bilimle elde tutulabileceği” yönündeki derin felsefesinin tasa oyulmuş halidir. Bu kurumda atılan sağlam temeller, Osmanlı ilmiye sınıfını yüzyıllar boyunca ayakta tutacak olan kurumsal omurgayı oluşturmuştur.Öğe Türü: Öğe , Nişancı Mehmed Paşa Vakfiyesi Üzerinden Osmanlı Külliye Sistemine Dair Mimari ve Toplumsal Bir İnceleme(İstanbul Üniversitesi, 2026) Ceran, İremBu çalışma, Osmanlı Klasik Dönem külliye mimarisinin geç örneklerinden biri olan Nişancı Mehmed Paşa Külliyesi’nin vakfiyesi üzerinden, mimarlık tarihi ve şehir tarihi bağlamında kapsamlı bir değerlendirme sunmaktadır. Bazı araştırmacılar tarafından Mimar Sinan’ın son eseri kabul edilen bu külliyenin birçok yapısı günümüze ulaşamamıştır. Bu sebeple, külliyenin mimari organizasyonu, sosyal ve vakıf sistemiyle bağlantılı işleyişi hakkında en önemli kaynak vakfiye metnidir. Bu çalışmada daha önce arşiv uzmanları tarafından transkripsiyonu yapılmış fakat yayımlanmamış vakfiyenin mimarlık tarihi açısından sistematik biçimde analiz edilerek içerdiği tarihî verilere eklenen değerlendirme ile vakfiyede yer alan bilgilerin birçok saha için yönlendirici nitelikte olması hedeflenmektedir. Çalışma kapsamında, vakfiyenin satır aralarından Mimar Sinan’ın Osmanlı sanatının zirvesinde tasarladığı son yapının inşa gerekçesi, mekânsal organizasyonu, görevlendirmeleri, toplumsal beklentileri ve dönemin mimari üslubuna dair çıkarımlar yapılmıştır. Böylece Mimar Sinan sonrası külliye kavrayışının devamlılığı ve dönemin devlet ricalinin mimariye müdahale biçimleri üzerine yeni bir katkı sunmak amaçlanmaktadır.Öğe Türü: Öğe , Efficient Olive Leaf Disease Detection via Hybrid Artificial Rabbit Optimization and Genetic Algorithm-Based Deep Feature Selection(MDPI, 2026) Türkmenoğlu, Cumali; Gündüz, Hakan; Gazioğlu, EmrullahArtificial intelligence (AI)-supported agricultural disease detection has become increasingly important for addressing global food security challenges. In this study, a hybrid metaheuristic optimization-based feature selection approach is proposed for the detection of peacock eye disease (Venturia oleaginea) on olive leaves. The proposed method combines Artificial Rabbit Optimization (ARO) and Genetic Algorithm (GA) strategies to balance global exploration and local exploitation during feature selection. Comprehensive experiments conducted on a dataset of 954 olive leaf images demonstrate that the proposed approach achieves an F1-score of 99.7% while reducing the feature dimensionality by 95%, selecting only 100 features from ResNet101. Statistical analysis confirms that the method significantly outperforms standalone GA and ARO approaches (p < 0.05, paired t-tests), demonstrating superior long-term convergence behavior and a 47–56% reduction in performance variance across repeated runs. Compared to existing approaches in the literature, the proposed method attains competitive or superior accuracy with substantially fewer features, indicating a marked reduction in computational complexity. These results suggest that the proposed hybrid feature selection framework has strong potential for deployment in resource-constrained agricultural monitoring scenarios, where efficient inference and reduced model complexity are critical.
