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.

Communities in DSpace

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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 3811
MSA University Campus 2
Images for MSA University " sites - building - landscape "

Recent Submissions

Inanimate objects back to life using AR
(2022-06) Ahmad Atef; Aya Mostafa; Ghada Abdelhady
Because of a lack of awareness about the history and heritage of ancient Egyptian civilization. Tourists sometimes suffer from the lack of knowledge of historical information about statues and hieroglyphic language inside museums. The forgotten Egyptian Museums need some actions and excitement to bring them back to life. Egypt has a treasure of museums that hide the beauty of history and cultural heritage. Industry 5.0 and the research introduce a new interactive image for those museums to back them to life, not only to re-open them but to enjoy and live in these ages and history to feel the experience. Augmented Reality (AR) will help those museums. It uses technology to superimpose images, text or sounds on top of what a person can already see. It uses a smartphone or tablet to alter the existing picture, via an app. The user stands in front of a scene and holds up their device. It will show them an altered version of reality. Augmented reality has found many unbelievable uses in various industries like the E-Commerce industry, Real Estate Industry, Prototyping Expenses, Education and Tourism. Our case study is using AR technology in tourism. AR is a powerful visualization tool. As it allows bringing an object or concept into a reality that is otherwise imagined, inaccessible or difficult to grasp, and can even help to make the invisible visible. In this way, this research collects abandoned Egyptian museums and explains how AR brings them to life. That will let you live the whole experience and let the community understand the cultural heritage with these examples of attractive case studies.
Educational Firmware Over-the-Air (FOTA) Kit for Automotive Systems in STEM Education
(Egyptian Association for Technological Development, 2024-11) Ghada Abdelhady; Emad H. Abdelmalak; Omar Elsehity
The growing complexity of automotive electronics and the increasing demand for hands-on STEM education highlight the need for practical tools that introduce students to cutting-edge technologies. Firmware Over-the-Air (FOTA) technology, widely used in the automotive industry for remote software updates, remains underutilized in educational contexts. To bridge this gap, we present the Educational Firmware Over-the-Air (FOTA) Kit, a comprehensive learning platform designed to teach foundational and advanced concepts in embedded systems, secure communication, and automotive technologies. The kit integrates hardware components such as Raspberry Pi, STM32 microcontrollers, and Controller Area Network (CAN) Bus interfaces with software modules covering bootloaders, encryption protocols, and an intuitive Graphical User Interface (GUI). These elements simplify complex automotive technologies for classroom use. Through interactive activities, including writing custom bootloaders, configuring CAN networks, and implementing AES encryption, students gain practical, real-world experience while developing technical proficiency. Initial deployment of the Educational FOTA Kit has demonstrated its effectiveness in enhancing student engagement, bridging theoretical knowledge and practical application. Quantitative results indicate improvements in problem-solving skills and a deeper understanding of secure firmware updates. By fostering innovation and technical expertise, this kit prepares students for emerging fields such as IoT, automotive engineering, and cybersecurity. The Educational FOTA Kit marks a significant advancement in STEM education, offering a dynamic and versatile platform aligned with the technological demands of modern industries.
Leveraging YOLOv8 and FaceNet for Enhanced Surveillance and Organization in Medicinal Warehouses
(National Research Centre, 2025-02-02) Ghada Abdelhady; Sameh Ayoub; Mohab Youssef
Medicinal warehouses face numerous challenges, including disorganization, theft, and human errors in inventory management, which result in economic losses and unauthorized access to controlled substances. To address these issues, this study presents an advanced system that integrates the "You Only Look Once (YOLO)" algorithm for real-time object detection and tracking, implemented on a Jetson Nano microprocessor with cameras and sensors. The system automates inventory management by organizing medicinal products, reducing human error, and monitoring access to controlled substances through ultrasonic sensors and theft detection mechanisms. Additionally, a machine learning model trained on historical inventory data predicts future stock demands, enabling proactive restocking to prevent shortages and maintain operational efficiency. YOLOv8 was selected for its superior accuracy and speed, achieving a mean Average Precision (mAP) of 0.923, alongside reductions in Box Loss, Class Loss, and Distribution Focal Loss (DFL). The results demonstrate high-accuracy detection and classification, even under challenging conditions, ensuring a reliable and scalable solution for medicinal warehouse management. This system provides enhanced security, operational efficiency, and predictive capabilities, contributing to better inventory control and public health outcomes. Future work will focus on improving system scalability, integrating real-time data streams, and refining predictive accuracy for broader adoption.
Enhancing Museum Experiences with Augmented Reality and Machine Learning: A Case Study of Egyptian Cultural Heritage
(Association of Scientific Research Technology and the Arts, 2025-01) Ghada Abdelhady; Ahmed Atef; Aya Mostafa
This study explores the application of augmented reality (AR) to enhance visitor engagement and accessibility in Egyptian museums, comparing two approaches: the Vuforia SDK and a deep learning model integrated with Unity. Addressing the need for innovative solutions to improve cultural heritage experiences, the research investigates how AR can deliver immersive and educational interactions with Egyptian artifacts. The Vuforia-based prototype offers structured content delivery using predefined image targets, while the deep learning model employs convolutional neural networks (CNNs) for real-time and adaptive object recognition across varied environments. User testing revealed that, although the Vuforia prototype is lightweight and user-friendly, the deep learning model outperformed it in accuracy and adaptability. However, its computational demands pose challenges for scalability. This paper highlights the transformative potential of deep learning-driven AR in cultural heritage and proposes strategies to optimize its adoption in the tourism sector.
A Novel Explainable AI Framework for Real-Time Cybersecurity Threat Detection and Mitigation
(Association of Scientific Research Technology and the Arts, 2025-01) Ghada Abdelhady
Cybersecurity remains a critical challenge as cyberattacks grow increasingly sophisticated and diverse. This paper presents a novel Explainable AI (XAI) framework for real-time detection and mitigation of cyber threats, including Distributed Denial of Service (DDoS) attacks, Shellcode exploitation, Reconnaissance, and Worm propagation. The framework employs advanced feature engineering and class-specific techniques to enhance detection accuracy, particularly for overlapping categories like DoS and Exploits. It integrates visual explainability tools, automates incident response processes, and seamlessly connects with Security Information and Event Management (SIEM) systems to support operational decision-making. Using eXtreme Gradient Boost (XGBoost) combined with SHapley Additive exPlanations (SHAP) for explainability, the system achieves both high detection accuracy and transparency. Additionally, a comparative analysis with Random Forest (RF) and Support Vector Machine (SVM) highlights the proposed framework's superior performance. Experimental results demonstrate an accuracy of 89% and an F1-score of 0.88, with strong detection capabilities for high-priority threats like Generic and Shellcode while maintaining high precision across all classes. This research underscores the potential of the framework to transform real-time cybersecurity by ensuring precise, transparent, and actionable threat detection.