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 , A Comprehensive Scientific Mapping of Academic Publishing: Conceptual Trends in The Field of Interior Architecture and Interior Design(Akademisyen Yayınevi, 2025) Burkut, Emine BanuInterior architecture and design have evolved from craft-based practices into academically recognized, research-driven disciplines that increasingly intersect with architecture, design studies, environmental psychology, and materials science. Over the past two decades, global transformations, including sustainability imperatives, digital design technologies, health and wellness concerns, and discourses of cultural identitiy, have reshaped both the practice and academic scholarship of the field. Because interior spaces are the primary environments in which individuals live, work, and interact, academic research has expanded beyond aesthetics to encompass human well-being, environmental performance, and social value (Ashour et al., 2021; Burkut, 2023). In the field of interior design, this method identifies research and conceptual trends (Tan et al., 2021; Rui et al., 2023; Walia & Singh, 2023; Beşkaya & Gökgöz, 2025; Fu & Keat, 2025). Despite this growing literature, the conceptual landscape of interior architecture and design remains fragmented. Research contributions are often scattered across a variety of journals and conferences, typically located in architecture, design, engineering, or interdisciplinary settings. This dispersion makes it difficult to trace the intellectual structure of the field, identify thematic clusters, and assess its evolution over time. Therefore, it is important to clarify knowledge domains, uncover conceptual connections, and conduct a systematic and comprehensive scientific mapping of academic publications.Öğe Türü: Öğe , Neuro-Architecture: Design Principles and Scientific Visualization(Akademisyen Yayınevi, 2025) Akbal, Beyza Nur; Burkut, Emine Banu; Yılmaz, NazendeNeuroarchitecture is an architectural principle that combines neuroscience and architecture. Neuroarchitecture examines the physical and psychological responses of users to space and the environment (Eberhard, 2009; Ritchie, 2020). Neuroarchitecture aims to produce spatial designs in response to these responses and to enhance individual psychological processes. Space design and environmental factors significantly influence human perception, mood, and behavior. Individuals interact with their environment, exhibiting responses based on sensory, physical, and past experiences. A person’s past memories and traumas influence their perception of space. This research examines neuro-architecture conceptually and theoretically. The study utilizes the scientific mapping method, a quantitative research method. This method reveals the conceptual, theoretical, and evolutionary development of the relevant literature through numerical data and frequencies. The findings of the research facilitate the emergence of influential publications on neuro-architecture in the current literature.Öğe Türü: Öğe , An Adaptive Hybrid Metaheuristic Algorithm for Lung Cancer in Pathological Image Segmentation(MDPI, 2026) Şahin, Muhammed Faruk; Anka, FerzatBackground/Objectives: Histopathological images are fundamental for the morphological diagnosis and subtyping of lung cancer. However, their high resolution, color diversity, and structural complexity make automated segmentation highly challenging. This study aims to address these challenges by developing a novel hybrid metaheuristic approach for multilevel image thresholding to enhance segmentation accuracy and computational efficiency. Methods: An adaptive hybrid metaheuristic algorithm, termed SCSOWOA, is proposed by integrating the Sand Cat Swarm Optimization (SCSO) algorithm with the Whale Optimization Algorithm (WOA). The algorithm combines the exploration capacity of SCSO with the exploitation strength of WOA in a sequential and adaptive manner. The model was evaluated on histopathological images of lung cancer from the LC25000 dataset with threshold levels ranging from 2 to 12, using PSNR, SSIM, and FSIM as performance metrics. Results: The proposed algorithm achieved stable and high-quality segmentation results, with average values of 27.9453 dB in PSNR, 0.8048 in SSIM, and 0.8361 in FSIM. At the threshold level of T = 12, SCSOWOA obtained the highest performance, with SSIM and FSIM scores of 0.9340 and 0.9542, respectively. Furthermore, it demonstrated the lowest average execution time of 1.3221 s, offering up to a 40% improvement in computational efficiency compared with other metaheuristic methods. Conclusions: The SCSOWOA algorithm effectively balances exploration and exploitation processes, providing high-accuracy, low-variance, and computationally efficient segmentation. These findings highlight its potential as a robust and practical solution for AI-assisted histopathological image analysis and lung cancer diagnosis systems.Öğe Türü: Öğe , Development of a Self-Efficacy Scale for Arabic Reading Comprehension Among High School Students(Sage, 2026) Akkuş, SaraThis study aimed to develop a self-efficacy scale to assess high school students’ perceived competence in Arabic reading comprehension. Accordingly, a draft version of the ‘‘High School Students’ Self-Efficacy Scale for Arabic Reading Comprehension’’ was constructed as a 5-point Likert-type instrument. Following expert evaluations (n = 8), the initial pool of 46 items was reduced to a 23-item trial form based on content validity analysis. Exploratory factor analysis (EFA) was conducted with data from 210 students, while confirmatory factor analysis (CFA) was performed with a separate sample of 473 students. In this study, EFA and CFA were performed on separate samples. While this is methodologically appropriate, it may have constrained the model’s fit. Moreover, the use of an all-male sample for the EFA presents a potential limitation in terms of demographic diversity. EFA results indicated a unidimensional structure. The CFA results demonstrated acceptable model fit indices (RMSEA = 0.06, x2/df = 2.59, GFI = 0.87, CFI = 0.95), confirming the construct validity of the scale. The internal consistency of the scale was found to be high, with a Cronbach’s alpha coefficient of .957. Although the scale demonstrated strong psychometric properties, the absence of gender diversity in the EFA sample and the lack of subsequent invariance testing are acknowledged as limitations. Nevertheless, despite these constraints, the developed scale represents a valuable tool for educators, researchers, and curriculum developers aiming to assess high school students’ self-efficacy in Arabic reading comprehension.Öğe Türü: Öğe , A Cybersecurity Method to Detect SQL Injection Attacks Using Heuristic‑Driven Feature Selection and Machine Learning Algorithms(Springer Nature, 2026) Arasteh, Bahman; Karimi, Mohammadbagher; Kuşetogulları, Hüseyin; Arasteh, Keyvan; Kiani, FarzadSQL injection is a serious security risk that allows attackers to access application databases. SQL injection attacks can be identified using various methods, including machine learning algorithms. Finding the top-performing features in the training dataset is a combinatorial optimization problem known to be NP-complete. Finding the dataset’s most effective and significant features is the goal of feature selection. This study aims to optimize the sensitivity, specificity, and accuracy of the SQL injection detection method. The first stage of the suggested method involved creating a unique training dataset with 13 characteristics. A binary form of the Whale Optimization Algorithm was suggested to find the most effective features in the dataset. An effective SQL injection detection system was developed by combining the whale algorithm as a feature selector with various machine learning techniques. The suggested SQL injection detector achieved 98.88% accuracy, 99.35% sensitivity, and a 98.83% F1-score using an artificial neural network and the whale optimizer. Using the proposed strategy to select about 31% of the features improved the performance of the attack detectors.
