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 3797
MSA University Campus 2
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Recent Submissions

Privacy-preserving intrusion detection in IoT smart homes using a federated hybrid 1D-CNN–LSTM model with explainable AI
(Nature Research, 2026-09-02) Ghada Abdelhady; Karim Wael Hussein; Islam Anwar Ali Gad
The proliferation of Internet of Things (IoT) devices in smart home environments has dramatically expanded the attack surface for cyber threats, particularly botnet-driven Distributed Denial of Service (DDoS) attacks. Centralized Intrusion Detection Systems (IDS) are ill-suited to this domain because they violate user privacy, introduce single points of failure, and incur prohibitive communication overhead. Federated Learning (FL) offers a compelling privacy-preserving alternative, yet existing FL-based IDS solutions either deploy convolutional or recurrent models in isolation, lack human-interpretable outputs, or neglect real-world deployability constraints. This paper proposes FedShield-IDS, a novel federated intrusion detection framework that integrates a hybrid one-dimensional Convolutional Neural Network with Long Short-Term Memory units to simultaneously capture spatial traffic fingerprints and long-range temporal attack dynamics across IoT edge devices. Model interpretability is addressed through the integration of SHapley Additive exPlanations (SHAP), enabling administrators to receive human-readable justifications for every detected anomaly. The system is trained and evaluated on the large-scale CICIoT2023 dataset, comprising 712,311 flow records spanning eight attack families including DDoS, DoS, Mirai, Reconnaissance, Spoofing, Injection, and Malware. A multi-stage preprocessing pipeline combining infinite-value imputation, logarithmic feature scaling, Min-Max normalization, temporal windowing, and localized SMOTE oversampling is applied within each federated client to address non-IID data and extreme class imbalance. Federated Averaging aggregates encrypted model updates across seven virtual IoT client nodes over five communication rounds without exchanging raw traffic data, under a formal threat model characterizing the system’s adversarial assumptions and data-confidentiality guarantees. Experimental results demonstrate a Mirai F1-score of 0.99, a DDoS precision of 0.97, and a global weighted F1-score of 0.76 across all eight classes. Comprehensive kernel-size, architecture, and preprocessing ablations confirm the necessity of each design choice, and independent cross-dataset evaluation on the Edge-IIoTset benchmark achieves 98.58% accuracy, demonstrating strong generalization beyond CICIoT2023. The framework achieves sub-500 ms threat mitigation, empirically confirmed via a mitigation-gate threshold sensitivity analysis, and generates SHAP-gated explanations for every alert, bridging the gap between high-accuracy detection and the transparency required for trustworthy smart-home security.
Analysis of real water samples with AQbD microextraction using thymol-based NADES: implications of uncontrolled hypertension risk in Egypt
(Royal Society of Chemistry, 2026-08-20) Heba T. Elbalkiny; Elsaiad Nasry; Marina Nayer; Sohaila Ahmed; Yara Osama
The global burden of hypertension and its severe form, resistant hypertension, affecting up to 20% of 1.4 billion adults worldwide, drives mass consumption of multiple drugs. Consequently, antihypertensive agents from different classes enter aquatic environments, yet simultaneous extraction of such chemically diverse drugs remains challenging. This work developed a green analytical method for the simultaneous determination of three model antihypertensive agents from distinct classes, namely, furosemide, atenolol, and lisinopril, in water samples. The method employed dispersive liquid–liquid microextraction using a thymol-based natural deep eutectic solvent selected for its superior hydrophobicity and tunability across varying analyte polarities and acid-base characteristics. Extraction was optimized via Box–Behnken design, followed by HPLC-UV analysis on a phenyl column with gradient elution (detection at 220 nm). The validated method showed good linearity (furosemide: 2–100 µg L−1, lisinopril: 5–100 µg L−1, and atenolol: 0.1–50 µg L−1) with LODs of 0.18, 0.66, and 0.031 µg L−1, respectively. The practical applicability of the developed method was evaluated by analyzing real water samples collected from multiple sites across Egypt. The results revealed detectable concentrations of the target pharmaceuticals in several samples, confirming the presence of these contaminants in Egyptian water systems. Specifically, furosemide was detected at 4.72 µg L−1, atenolol at 0.43 µg L−1, and lisinopril at 7.21 µg L−1 in samples collected from Giza. A comprehensive greenness assessment using the GAPI, AGREE, BAGI, and EVG tools confirmed strong alignment with sustainable chemistry principles. By enabling multi-class extraction of antihypertensive drugs with a low-ecological-footprint NADES-based protocol, this method addresses a critical gap as no prior NADES-based method exists for these three therapeutic classes together, supporting environmental monitoring and clean water objectives.
HeteroMetaNet: A multimodal meta-learning framework for ischemic stroke prediction from heterogeneous clinical and radiological data
(Elsevier Ltd, 2027-01-04) Raghda E. Ali; Reda A. Elkhoribi; Ehab H. Ezzat; Farid A. Moussa
Accurate and timely stroke detection remains a major clinical challenge due to the heterogeneous and multimodal nature of patient data. Most existing deep learning approaches rely solely on either imaging or structured clinical records, which limits their robustness and generalizability in real-world settings. This study introduces two multimodal architectures, HeteroFuseNet and HeteroMetaNet, designed to integrate non-contrast CT brain images with structured electronic health record (EHR) variables within a unified framework, referred to as HeteroStroke. HeteroFuseNet employs a PCA-enhanced mid-level fusion strategy to learn joint representations of deep visual features extracted using ResNet-50, EfficientNet-B0, and Swin Transformer, combined with structured clinical embeddings. In contrast, HeteroMetaNet adopts a lightweight meta-learning approach that adaptively integrates modality-specific predictions from these vision models alongside clinical features to achieve robust, context-aware stroke classification. Experimental results demonstrate that both proposed models outperform image-only baselines, particularly in clinically heterogeneous cases, highlighting the effectiveness of multimodal fusion in enhancing diagnostic reliability. This work underscores the potential of cross-modal learning to support precision medicine in stroke care. Future work will extend the framework toward differentiating ischemic and hemorrhagic stroke subtypes for improved clinical decision support.
Retraction Note: The Potential Role of Zinc Oxide Nanoparticles in MicroRNAs Dysregulation in STZ-Induced Type 2 Diabetes in Rats
(Springer, 2026-08-22) Mohamed S. Othman; Mohamed M. Hafez; Ahmed E. Abdel Moneim
The Editors-in-Chief have retracted this article. After publication, concerns were raised regarding the similarity of Panel C in Fig. 9 and Panel B in Fig. 6 in [1], as the two images report the results of different experiments. In addition, an investigation by the Publisher could not conclusively determine the authenticity of the ethics approval documentation provided by the authors. The Editors-in-Chief therefore no longer have confidence in the research presented in this work. Ahmed E. Abdel Moneim has stated on behalf of all authors that they disagree to this retraction.
A novel technique for automatic hybrid modulation classification in UWA communications using 3D constellation diagrams
(Nature Research, 2026-08-14) Mohamed A.Abdel-Moneim; Khalil F. Ramadan; El-Sayed M. El-Rabaie; Fathi E. Abd El-Samie; Nariman Abdel-Salam
Underwater acoustic (UWA) communication systems operate in severe channel impairments, including strong multipath propagation, long delay spreads, frequency selectivity, Doppler effects, and high ambient noise. These challenges significantly complicate automatic modulation classification (AMC), especially in dense underwater networks where interference further degrades performance. In this paper, we investigate AMC for frequency-indexed three-dimensional hybrid modulation schemes, namely frequency-phase keying (FPK) and frequency quadrature amplitude modulation (FQAM), in UWA environments. While AMC has been extensively studied for conventional modulation formats, its application to frequency-indexed hybrid modulation schemes in UWA communication systems has received comparatively limited attention in the existing literature. The proposed approach depends on a three-dimensional (3D) constellation representation for robust feature extraction and modulation discrimination. The resulting 3D signal representations are converted into image-based inputs and processed with deep convolutional neural network (CNN) models, including AlexNet, VGG-19, and ResNet50, to automatically extract discriminative features and enable reliable classification. The proposed framework is evaluated under both single-carrier (SC) and orthogonal frequency-division multiplexing (OFDM) transmission schemes to comprehensively assess its robustness and adaptability. Simulation results demonstrate accurate modulation recognition under severe UWA conditions and low signal-to-noise ratio (SNR), confirming the effectiveness of combining hybrid modulation with deep-learning-based AMC for next-generation UWA communication systems.