İZÜ Araştırma ve Akademik Performans Sistemi
DSpace@İZÜ, İstanbul Sabahattin Zaim Üniversitesi’nin bilimsel araştırma ve akademik performansını izleme, analiz etme ve raporlama süreçlerini tek çatı altında buluşturan bütünleşik bilgi sistemidir.
İndekslere Göre Dağılım
Yıllara Göre Dağılım
Türlere Göre Dağılım
Güncel Gönderiler
Öğe Türü: Yayın , The Importance of Family Participation in Homework: Understanding the Relationship Between Student Homework Behaviors and Academic Achievement by School Level(Springer, 2025) Avcı, Süleyman; Özgenel, Mustafa; Avcu, Akif; Özgenel, MustafaThe purpose of this study was to examine in detail the relationship between per-ceived parental involvement in homework (content-oriented and autonomy-oriented forms of involvement) and students’ homework behavior (homework time, home-work time management, and amount of homework completed). The study also looked at how the relationships between these variables change between middle school and high school. The study was conducted on 528 middle and high school students in grades 5–12. The relationships between the variables used in the study were structured using a theoretical model and tested using the structural equation model method. The results of the study show that parental involvement in home-work has a positive relationship with students’ homework behaviour. In particu-lar, time management skills have the strongest relationship with parental involve-ment among the three homework behaviors. However, while there was no direct relationship between parental involvement and overall academic achievement, parental involvement in homework showed a positive relationship with academic achievement through homework completion and time management. At the second-ary school level, there is a positive relationship between students’ homework time, homework completion and time management skills and academic achievement. Stu-dents’ homework completion stood out as the variable with the strongest relation-ship with academic achievement. These findings suggest that parental involvement in the homework process, especially at the secondary school level, can help students to manage their homework more effectively and be more successful academically.Öğe Türü: Yayın , Novel Nonlinear PI Controller Using Metaheuristic Algorithms for Speed Control of Wind Turbine Systems(International Information and Engineering Technology Association, 2025) Arabi, Marwa; Youcef, Zennir; Bounezour, Hichem; Benghanem, Mohamed S.; Garcia, J. E.S.; Wadi, Mohammed; Wadi, MohammedWind turbines operate under highly dynamic conditions influenced by unpredictable wind profiles and external disturbances. The nonlinear characteristics of their dynamic models further complicate their modeling and control. This research focuses on optimizing the power output of a Wind Energy Conversion System (WECS) equipped with a Permanent Magnet Synchronous Generator (PMSG). To achieve this, a Maximum Power Point Tracking (MPPT) strategy is developed, integrating an innovative nonlinear PI controller. The parameters of this controller are fine-tuned using advanced meta-heuristic optimization techniques, including Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Golden Jackal Optimization (GJO). Simulation results highlight the superior performance of the GJO-NLPI controller, demonstrating exceptional accuracy and rapid response in regulating mechanical rotation speed, while effectively reducing overshoot. The proposed control architecture showcases significant advancements in power extraction efficiency and dynamic performance.Öğe Türü: Yayın , Effects of ZrN and DLC coatings on morphostructural, corrosion, cell viability, and antibacterial properties of Ti6Al4V scaffolds(Springer, 2025) Aslan, Naim; Aksakal, Bünyamin; Cihangir, Salih; Fidan, Ferzan; Yilmazer, Yasemin; Yılmazer, YaseminLightweight and functional metallic materials are consistently favored and attract significant attention in the biomedical field. This study investigates the influence of ZrN and DLC coatings on Ti6Al4V scaffolds with varying porosities (40%, 50%, 60%) for biomedical applications. PVD-deposited coatings exhibited distinct growth mechanisms: High-resolution SEM-FIB analyses revealed columnar ZrN structures due to limited adatom mobility, while DLC formed conformal, amorphous layers via high-energy carbon species. Corrosion resistance decreased in 40% porous DLC and 60% porous ZrN-coated samples, likely due to coating defects and increased electrolyte access through open pores. ZrN coatings showed superior antibacterial activity and attributed to surface reactivity and potential ion release. In contrast, the uncoated 60% porous scaffolds demonstrated highest cell viability, suggesting that excessive coating on highly porous structures may hinder cell–material interactions. Furthermore, the cytotoxicity evaluations indicated that uncoated scaffolds exhibited the highest cell viability (77.51%) at 60% porosity after 48 hours, with ZrN coatings demonstrating better efficacy compared to DLC in the 50% and 60% porosity groups.Öğe Türü: Yayın , Precision Agriculture Using a Two-Tier ML Model: Integrating aKNCN Soil Classification with ELM-mBOA Yield Prediction(Springer, 2025) Naeem, Awad Bin; Senapati, Biswa Ranjan; Rasheed, Jawad; Abid, Fazeel; Alsubai, Shtwai; Rasheed, Jawad;Accurate crop yield prediction is critical for sustainable agricultural planning and resource optimization, especially amidincreasing food demand and climate variability. This study proposes a novel two-tiered machine learning (ML) frameworkthat integrates IoT-based soil data with advanced classification and regression models to enhance prediction accuracy. Inthe first tier, an Adaptive k-Nearest Centroid Neighbour (aKNCN) classifier evaluates soil quality based on key nutrientmetrics. The second tier utilizes an Extreme Learning Machine (ELM) optimized via the modified Butterfly OptimizationAlgorithm (mBOA) to forecast crop yields, incorporating both soil quality and agro-environmental factors. The systemis trained and validated on a publicly available Indian crop production dataset containing 10,000 samples across majorcrops (wheat, maize, rice), with features including soil moisture, temperature, and rainfall. Feature selection is performedusing Correlation-Based Feature Selection (CBFA) and Variance Inflation Factor (VIF) methods to reduce noise and mul-ticollinearity. Experimental results demonstrate that the proposed aKNCN-ELM-mBOA model significantly outperformstraditional ML models—such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Gradient Boosting(GB), and Random Forest (RF)—in terms of error metrics including Mean Absolute Error (MAE), Root Mean SquaredError (RMSE), Mean Absolute Percentage Error (MAPE), and R². The model achieves a notably low RMSE of 0.301and MAPE of 3.932, alongside a high R² score of 0.817, indicating strong generalization. This approach underscores thepotential of hybrid ML systems, enriched by IoT-driven data and robust optimization, to drive precision agriculture andinformed decision-making. Future work may involve time series forecasting and scaling the model with real-time sensordata for broader deployment.Öğe Türü: Yayın , Bridging Reality and Synthetics: Optimizing Image Classification with Hybrid AI-Generated and Real-World Datasets(Springer, 2025) Hasan Alabed, Abdallah Tariq; Rasheed, Jawad; Yeşiltepe, Mirsat; Alsubai, Shtwai; Aşuroğlu, Tunç; Rasheed, Jawad;The rapidly growing revolution of generative Artificial Intelligence software has moved into the counseling and disseminating synthetic images, thereby establishing a new paradigm for machine learning models. This study investigates the impact of combining real-world and AI-generated synthetic images on the performance of image classification models. Using three traffic-related datasets—potholes, speed bumps, and traffic lights—we applied data augmentation and tested seven configurations with varying real-to-synthetic image ratios. The DenseNet201 model, fine-tuned with the Adam optimizer, was used for all experiments. Results show that a 1:3 real-to-synthetic ratio enhances classification accuracy and generalization, with the highest validation accuracy reaching 97.36%. Our findings demonstrate that synthetic data, when properly integrated, serves as a cost-effective and scalable complement to real data, especially in scenarios with limited labeled samples.
