Mr. Mohammed Abdalla | Intelligent Transportation | Best Paper Award

Mr. Mohammed Abdalla, Beni-Suef University, Egypt

Dr. Mohammed Abdalla Mahmoud Youssif 🇪🇬 is a seasoned technology leader and current Head of Development at Giza Systems 🏢. With over 15 years of experience in software development 💻, he has excelled in managing teams, leading innovative projects, and delivering smart solutions 🌐. He holds B.Sc., M.Sc., and Ph.D. degrees from Cairo University 🎓 in computer science and engineering. His expertise includes big data 📊, machine learning 🤖, and smart city applications 🏙️. Passionate about future tech, Dr. Youssif is also active in academia with 20+ research publications 📚 and an online presence via YouTube and LinkedIn 🎥💼.

Professional Profile

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Education and Experience 

Dr. Mohammed Abdalla earned his B.Sc., M.Sc., and Ph.D. in Computer Science and Engineering from Cairo University 🎓. With more than 15 years of hands-on software development experience 💻, he has contributed to a wide variety of business projects ranging from enterprise platforms to smart city solutions 🌐. He currently leads development teams at Giza Systems 🏢, where he focuses on innovation, resource management, and technical excellence 🚀. His academic background is strongly tied to real-world applications, enabling him to bridge research and industry with a practical edge 🔗.

Professional Development 

Dr. Youssif’s career reflects consistent professional growth in both technical and leadership domains 🔧👨‍💼. Starting as a software developer 💻, he quickly climbed the ranks through a combination of innovation, problem-solving, and people management. As Development Head at Giza Systems 🏢, he now mentors engineers, allocates project resources 📅, and drives the development of cutting-edge solutions 🚀. His commitment to continuous learning and application of emerging technologies, such as big data 📊 and AI 🤖, has positioned him as a key contributor in Egypt’s digital transformation journey 🇪🇬.

Research Focus 

Dr. Mohammed Abdalla’s research is deeply rooted in cutting-edge technologies, especially big data management 📊, artificial intelligence 🤖, and machine learning algorithms 🧠. He places a particular focus on smart city applications 🌆, developing analytics tools and intelligent systems to enhance urban efficiency and sustainability 🚦🏙️. His work bridges academic research and practical implementation, ensuring innovations can be adopted in real-world scenarios. His 20+ publications 📚 reflect a commitment to solving complex societal problems through technology 💡. He aims to harness data and digital intelligence for smarter urban environments and better quality of life 🏘️.

Awards and Honors 

While Dr. Mohammed Abdalla is still building his list of formal recognitions, his contributions to smart city tech and software innovation are widely respected 🌍. As a speaker, team leader, and contributor to international journals and conferences 📘, he is regarded as a thought leader in big data and machine learning fields 🧠. His position as Development Head at Giza Systems is a testament to his technical and managerial excellence 🏢. His active online presence via YouTube and LinkedIn helps mentor younger professionals 📽️💼, adding to his community impact and informal recognition within the tech ecosystem 👏.

Publication Top Notes

1. Crisis Management Art from the Risks to the Control: A Review of Methods and Directions

📚 Authors: A.H. Mohammed Abdalla, Louai Alarabi
📘 Journal: Information (Vol. 42, 2021)
📈 Citations: 42
📄 Summary:
This review outlines the landscape of crisis management frameworks, emphasizing how organizations can transition from identifying risks to establishing control mechanisms. It evaluates methodologies for risk assessment, communication, and coordination, providing a comprehensive guide for practitioners and researchers seeking to improve resilience and decision-making in crises. The paper synthesizes real-world implementations with theoretical models to chart future research directions in crisis response systems.

2. TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

📚 Authors: Louai Alarabi, S. Basalamah, A. Hendawi, Mohammed Abdalla
📘 Journal: Information (Vol. 12, No. 5, 2021)
📈 Citations: 21
📄 Summary:
This study presents TraceAll, an innovative real-time contact tracing system that leverages indoor trajectory data to identify potential exposure events. It uses spatial indexing and real-time analytics to provide fast and scalable tracing, crucial during health crises like COVID-19. The paper discusses system architecture, algorithms, and a deployment case study, demonstrating its effectiveness in high-density areas.

3. DeepMotions: A Deep Learning System for Path Prediction Using Similar Motions

📚 Authors: Mohammed Abdalla, Abdeltawab Hendawi, Hoda M.O. Mokhtar, Neveen ElGamal
📘 Journal: IEEE Access, 2020
📈 Citations: 16
📄 Summary:
DeepMotions is a path prediction framework that applies deep learning to movement data, identifying similar motion patterns to predict future trajectories of moving objects. It integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to model spatial-temporal patterns. Applications range from pedestrian prediction to intelligent transportation systems.

4. FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

📚 Authors: E.O. Eldawy, A. Hendawi, Mohammed Abdalla, Hoda M.O. Mokhtar
📘 Journal: ISPRS International Journal of Geo-Information (Vol. 10, No. 11, Article 767, 2021)
📈 Citations: 13
📄 Summary:
FraudMove introduces a real-time framework for detecting fraudulent behavior based on vehicle movement anomalies. Using trajectory outlier detection, the system identifies unexpected routes or suspicious driving patterns that may indicate fraud, such as in ride-sharing or insurance claims. The framework blends spatio-temporal clustering and machine learning models for accurate fraud detection.

5. HarmonyMoves: A Unified Prediction Approach for Moving Object Future Path

📚 Authors: Mohammed Abdalla, Hoda M.O. Mokhtar
📘 Journal: International Journal of Advanced Computer Science and Applications, pp. 637–644, 2020
📈 Citations: 7
📄 Summary:
This research proposes HarmonyMoves, a hybrid model that integrates historical trajectory data with environmental context to predict the future paths of moving entities (e.g., vehicles, pedestrians). Unlike previous models that relied solely on movement data, this approach harmonizes contextual and historical data for robust, real-time trajectory prediction.

Conclusion

Dr. Mohammed Abdalla’s contributions meet and exceed the standards typically required for Best Paper Awards at prestigious conferences and journals. His research is characterized by technical innovation, interdisciplinary applications, practical impact, and high citation potential. He is especially commendable for producing systems that combine machine learning with real-world problem solving, such as contact tracing and mobility analytics.