MSA Repository "MSAR"

MSAR University's Digital Repository is a documentation and digitization of all university outcomes that are of effective value in the scientific and academic community and reflects the university's image, work, and effective contribution to society Through MSAR Digital Repository, the university managed to collect, store, archive and publish digital content - including documents, audio files, images and data sets - all in a safe place. MSAR is one of the strongest University Digital Repositories in Egypt and documented in the DSPACE community with its latest versions.

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RESEARCH & LANGUAGES CENTERS 18

Recent Submissions

Decomposition-based multi-modal image fusion for breast cancer classification using AlexNet and MCFO filter
(Springer, 2026-04-07) Basem Ashraf; El-Sayed M. El-Rabaie; Nariman Abdel-Salam
Breast cancer is one of the most common and deadly cancers, affecting millions worldwide. Early and accurate detection is essential for effective treatment and improved patient outcomes. Advances in medical imaging technologies, such as Digital Mammography (DM), Ultrasound (US), and Magnetic Resonance Imaging (MRI) provide clinicians with detailed information about breast tumors and surrounding tissues. However, merging and analyzing these multimodal images pose challenges. Medical image fusion combines images from different modalities to improve quality, reduce noise and redundancy, and support more precise clinical decisions. In this study, three models were developed to evaluate feature extraction strategies: Model A uses an AlexNet architecture, Model B employs a LeNet-5 architecture, and Model C incorporates a DenseNet-121 architecture. All models are integrated with a decomposition method, such as PCA or DWT, for image fusion into three main categories: normal, benign, or malignant. The Modified Central Forced Optimization (MCFO) filter is employed to enhance diagnostic accuracy. Our framework was tested on a new dataset from Baheya Hospital in Egypt, which includes high-quality, annotated images. Results show that combining DWT-based methods with AlexNet and the MCFO filter achieves top performance, with an accuracy of 97.4%, a precision of 95%, a Recall of 96%, a F1 Score of 93%, and an ROC score of 96.95%, with minimal loss, demonstrating strong generalization and stability across epochs. These findings highlight the superior performance of the DWT-based approach with AlexNet and MCFO compared to other methods.
TransSiamUNet Based Transformer-Augmented Siamese-U-Net for Precise Change Detection in Satellite Imagery
(Nature Research, 2026-04-07) Farid Ali; Soha Safwat Labib; Ayat Mahmoud; Ibrahim Eldesouky Fattoh
Identifying changes in satellite images is vital for tasks like tracking land cover and land use, evaluating disaster impacts, and conducting military surveillance. Although conventional techniques for detecting changes in multispectral remote sensing data are commonly applied, they often fail to meet the requirements for reliability and precision. Recently, deep learning methods have emerged, providing more accurate and effective solutions for monitoring environmental transformations and urban expansion in satellite imagery. This paper introduces TransSiamUNet, a deep learning architecture that combines Siamese networks, U-Net segmentation, and Vision Transformers (ViT) for high-precision change detection. The model processes paired Sentinel-2 images via a tailored preprocessing pipeline and integrates local and global feature extraction for pixel-level change segmentation. On the OSCD benchmark, TransSiamUNet achieves an accuracy of 0.94, surpassing the Siamese network (0.86), U-Net (0.84), and Siamese+U-Net hybrid (0.91). These results demonstrate the model’s superior capability in detecting fine-grained urban and environmental changes, highlighting its suitability for real-world remote sensing applications.
Evaluation of marginal and internal adaptation of implant-supported PEEK crowns fabricated by 3D printing, milling, and pressing: a micro-CT analysis
(BioMed Central Ltd, 2026-04-09) Aliaa Ibrahim Mahrous; Mostafa El-Shazly; Mahitab Mansour; Alshaimaa Ahmed Shabaan; Mohamed Mokhtar; Ahmed Tawfik; Mohamed Mostafa Radwan
Objectives: This study aimed to evaluate and compare the marginal adaptation and internal gap of implant-supported crowns fabricated from polyetheretherketone (PEEK) using CAD-CAM milling, heat pressing, and 3Dprinting, employing non-destructive micro-computed tomography (µCT). Methods: This in-vitro study used thirty PEEK crowns (n = 10 per group) that were fabricated using CAD-CAM milling (MP), heat pressing (PP), and 3D printing (3DP) and seated on standardized zirconia abutments. Marginal and internal gaps were quantitatively assessed using high-resolution µCT scanning (voxel size: 9.2 μm) at 12 predetermined locations per crown in sagittal and coronal planes. Measurements included marginal gaps (mesial and distal), finish line gaps, and internal gaps at axial walls, occlusal surfaces, and internal line angles. Non-parametric statistical tests (KruskalWallis and Dunn's post hoc with FDR correction) were applied, with significance set at P<0.05. Results: All measurement sites showed statistically significant differences between the groups (P<0.001), with large effect sizes. Milled crowns exhibited the smallest occlusal gaps and superior adaptation at the occlusal surface (P<0.001), while 3D-printed crowns demonstrated the best adaptation along axial walls and internal angles. Pressed crowns consistently showed the largest marginal and internal gaps across most regions. All fabrication techniques demonstrated marginal gap values within the clinically acceptable threshold (<120 µm); however, statistically significant differences were observed in both marginal and internal adaptation among the three groups (P < 0.001). Conclusions: Fabrication technique significantly affects the marginal adaptation and internal gap of implant-supported PEEK crowns. While milled crowns showed optimal occlusal adaptation, 3D-printed crowns provided the best internal conformity along axial surfaces. Pressed PEEK restorations exhibited the poorest adaptation. These findings underscore the importance of technique selection in optimizing clinical outcomes of PEEK-based implant prostheses. Clinical significance: Marginal adaptation and internal fit affect the biological and mechanical success of implant crowns. Milling and 3D printing showed better adaptation than heat pressing, supporting techniques that improve longevity and reduce complications.
Comparative performance evaluation of meta-heuristic optimization algorithms for tuning PID gains in dual-axis solar tracking systems
(SAGE Publications Ltd, 2026-04-08) Mohamed Ibrahem Taha; Mohamed A. Kamel; Ehab Said; Wael Elmayyah
This work addresses the growing need for energy-efficient and accurate control in solar tracking systems, where precise alignment with the sun must be achieved without excessive actuator energy consumption. To this end, a novel proportional-integral-derivative (PID) controller tuning framework is proposed based on a dual-objective cost function that simultaneously minimizes both the integral time-weighted absolute error (ITAE) and the control effort, where the first objective is a measure of tracking accuracy, and the latter serves as a normalized approximation of control energy consumption. First, a complete model of the dual-axis solar tracking system is presented. Then, a PID controller is applied to minimize the tracking error. Next, the PID gains tuning is formulated as a multi-objective optimization problem, and five recent metaheuristic algorithms are applied to solve this problem. These algorithms are the Grey Wolf optimizer (GWO), the Aquila Optimizer (AO), the Manta Ray foraging optimization (MRFO), the Harris Hawks Optimization (HHO), and the gradient-based optimizer (GBO). All these algorithms are applied under consistent settings and benchmarked using 50 Monte Carlo simulations. Besides, they are compared based on their ability to achieve the best and mean solutions, standard deviation, computational effort, number of iterations, and convergence behavior. From the perspective of controller performance, the evaluation includes overshoot, settling time, steady-state error, and control energy consumption. Under these criteria, the GWO achieved the best cost values. All algorithms, however, exhibited overshoot limited to below 0.06% and settling times under 2 s. While from the perspective of algorithm performance, AO demonstrated the fastest average run-time and GWO was the lowest standard deviation, while MRFO achieved the lowest overshoot and GBO achieved the minimum energy consumption. The proposed framework outperforms existing approaches by integrating actuator energy into the control objective and validating statistical robustness through extensive simulation, thus offering a reliable, energy-aware strategy for real-time solar tracking deployment.
Response to Letter to the Editor regarding, “Advancements in artificial intelligence algorithms for dental implant identification”
(The Journal of Prosthetic Dentistry, 2026-04-02) Ahmed Yaseen Alqutaibi; Radhwan S. Algabri; Dina Elawady; Wafaa Ibrahim Ibrahim
Our conclusions were deliberately cautious and forward-looking, emphasizing the need for larger, more diverse datasets, external validation, and methodological standardization in future AI research on implant identification. We also noted that with continued advancements in technology and research design, AI models have the potential to accurately identify dental implant systems from radiographs and enhance patient care. We fully agree that future studies should include subgroup analyses by imaging modality and implant brand, as well as real-world clinical validation, to further strengthen the evidence base for AI integration in implant dentistry.