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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MSA Journals 140
A Full content for MSA university Faculties Journals
MSA Theses 5
A digital collection of MSA University postgraduate theses, including PhD and Master’s theses, organized by academic degree and faculty.
MSA University Academic Graduation Projects 1153
A Full content for msa university Distinguished Graduation Projects Yearbook
MSA University Academic Research 3741
MSA University Campus 2
Images for MSA University " sites - building - landscape "

Recent Submissions

Deep Learning Approaches for Infant Activity Recognition: a Comparative Study of Sequence Models and Ensemble Learning Techniques
(Springer International Publishing AG, 2026-05-01) Sohaila Ashraf; May Beshir; Shimaa Abd EL-Rahim Abd El-Aty; Moamen Zaher; Marwa Solayman
Supporting healthy growth and cognitive development in infants necessitates accurate recognition and monitoring of developmental milestones during the first year of life. Effective monitoring plays a pivotal role in this process, enabling parents and caregivers to provide personalized support and promote overall well-being. Although Human Activity Recognition (HAR) has been extensively studied, the majority of research has primarily targeted adult populations, leaving infant-specific HAR under-explored. This research advances infant activity recognition through three major contributions: (1) enhancing an existing dataset under the supervision of a pediatric physiotherapist to ensure clinical relevance and accuracy, (2) conducting a comprehensive evaluation of different sequence deep learning models, and (3) investigating the efficacy of ensemble learning through four distinct strategies. Experimental results demonstrate that ensemble learning outperforms individual models, achieving a 4.87% improvement in recall over the best-performing baseline, CNN-LSTM, and a 5.31% improvement over the most stable model, Bi-LSTM. These findings offer promising implications for the design of intelligent infant monitoring systems aimed at early detection of developmental milestones and improved longitudinal assessment.
A comparative evaluation of machine translation vs. human translation for legal texts: A case study of translation between English and Arabic
(Faculty of Modern Languages and Literatures, Adam Mickiewicz University, 2025-09-19) Noureldin Mohamed ABDELAAL; Islam AL SAWI
This study examines the comparative accuracy and fluency of Neural Machine Translations (NMTs) and Language Model-based translations (LMBTs), represented by ChatGPT and Google Translate (GT), in legal texts translations. Texts from Farahaty's "Arabic-English-Arabic Legal Translation", sourced from primary texts cited in the book and translated by scholars such as Hatim, Shunnaq, Buckley, and Farahaty were used as benchmarks for human translation (HT). Sixteen diverse texts encompassing various legal discourse subgenres were selected for analysis, with all Arabic in-text examples transliterated using the Library of Congress (LOC) system. Qualitative analysis was conducted to assess the extent to which NMTs and LLMs match HT in accuracy and fluency. The study also investigated the similarities and differences between ChatGPT and GT in their translation outputs. Findings highlight HT's superiority in producing precise, stylistically appropriate translations, compared to the challenges faced by NMTs and LLMs in capturing legal terminology and subtle linguistic nuances. Despite variations, both ChatGPT and GT demonstrate efficiency and context sensitivity, suggesting their potential as valuable tools when coupled with human post-editing. The study concludes by advocating for a hybrid approach that leverages the strengths of automated translation systems and human expertise to enhance cross-linguistic legal communication.
Exchange Rate Policy, Transportation Costs, and Inflation Dynamics: Implications for Sustainable Economic Management in Egypt
(World Association for Sustainable Development, 2026-05-20) Karim Soliman; Bandar Altubaishe; Doaa Mohamed Salman
Purpose: This study focuses on determining the impact of exchange rate policy on transportation costs and inflation dynamics in Egypt, with implications on sustainable economic management. Design/methodology/approach: Using annual time-series data (2014-2023) from the Central Bank of Egypt, World Bank and International Monetary Fund (IMF), we use regression analysis techniques to estimate the effects of real interest rates, official exchange rates and exchange rate liberalisation on the Consumer Price Index for Transportation. Findings: Real interest rates significantly predict transportation costs (B=4.75, p<0.01). Exchange rate liberalisation has the biggest coefficient (beta=0.525) but it is not statistically significant, indicating the multicollinearity problem or low power. The official exchange rate does not matter a great deal. Descriptive statistics show high levels of volatility in all the indicators after devaluations. Originality/value: This study advances the literature by isolating transportation costs as a distinct exchange rate transmission mechanism in Egypt and examining its sustainability implications. Practical implications: Policy-makers should co-ordinate exchange rate flexibility with monetary policy, diversify energy inputs, and strengthen social safety nets. Research limitations: Small sample size and single-country focus limit generalisability.
FRunetm: Enhancing Biomedical Nucleus Segmentation with CBAM-SE Attention
(Springer International Publishing AG, 2026-05-01) Menna Elgabry; Ali Hamdi; Mohamed Saiedur
Nucleus segmentation is fundamental for interpreting biomedical images, which further enables the analysis of cell structures and disease recognition. In this research, we pro pose Fourier Residual UNet m (FRunetm), an enhanced version of the Fourier Residual UNet (FRunet) architecture enhanced with the Convolutional Block Attention Module Squeeze and excitation attention (CBAM-SE) attention mechanism, for improving segmentation precision on complex biomedical images. The FRunetm model was compared with three other deep learning models, which are Classic U-Net, SE U-Net, FRunet with a classic attention mechanism. All models were evaluated based on a spectrum of performance metrics, including accuracy, Dice coefficient, F1 score, loss, mean intersect over union (IoU), precision, and recall. Results show a marked segmentation performance improvement with the U-Net based architecture that employs the sophisticated attention mechanism (CBAM-SE). The current work outlines the performance of several models and the potential impact on the segmentation of medical images.
Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation
(Springer International Publishing AG, 2026-05-01) Joy Naoum; Revana Salama; Ali Hamdi
Oral cancer is frequently diagnosed at later stages due to its similarity to other lesions. Existing research on computer-aided diagnosis has made progress using deep learning; however, most approaches remain limited by small, imbalanced datasets and a dependence on single-modality features, which restricts model generalization in real-world clinical settings. To address these limitations, this study proposes a novel data-augmentation–driven multimodal feature-fusion framework integrated within a (Vision Recognition)VR-assisted oral cancer recognition system. Our method combines extensive data-centric augmentation with fused clinical and image-based representations to enhance model robustness and reduce diagnostic ambiguity. Using a stratified training pipeline and an EfficientNetV2-B1 backbone, the system improves feature diversity, mitigates imbalance, and strengthens the learned multimodal embedding. Experimental evaluation demonstrates that the proposed framework achieves an overall accuracy of 82.57% on 2 classes, 65.13% on 3 classes, and 54.97% on 4 classes, outperforming traditional single-stream CNN models. These results highlight the effectiveness of multimodal feature fusion combined with strategic augmentation for reliable early oral cancer lesion recognition and serve as a foundation for immersive VR-based clinical decision-support tools.