Best Researcher Award
Amr A. Mohy
Arab Academy for Science, Technology & Maritime Transport, Egypt
Amr A. Mohy is an emerging researcher whose scholarly activities focus on data science, artificial intelligence, construction engineering analytics, and computational decision-support systems. His research portfolio reflects interdisciplinary applications of machine learning, graph neural networks, computer vision, and predictive analytics to improve safety, cost estimation, procurement, and operational efficiency within construction engineering and management. With publications indexed in international scholarly databases and a growing citation record, his work contributes to the integration of intelligent analytical methods into engineering practice.[1]
Abstract
This article presents a concise academic overview of Amr A. Mohy’s research achievements and evaluates their relevance to the Best Researcher Award. His publications demonstrate growing expertise in intelligent construction systems, machine learning, deep learning, graph attention networks, predictive analytics, and uncertainty quantification. Recent studies investigate construction safety, procurement optimization, and cost prediction while combining engineering knowledge with modern data-driven methodologies. These contributions illustrate an interdisciplinary research direction aligned with contemporary developments in data science and digital engineering.[2]
Keywords
Data Science, Construction Analytics, Machine Learning, Computer Vision, Graph Attention Networks, Safety Management, Predictive Modeling, Research Evaluation.
Introduction
Modern engineering increasingly depends on artificial intelligence and analytical computing for solving practical challenges involving safety, scheduling, procurement, and project management. Amr A. Mohy’s research reflects this transition by applying advanced computational methods to complex construction environments. His work emphasizes evidence-based decision making, interpretable predictive models, and scalable analytical frameworks capable of supporting infrastructure management while encouraging interdisciplinary collaboration between engineering and computer science.[3]
Research Profile
According to available bibliometric indicators, the researcher has produced six indexed publications, accumulated twenty-one citations, and achieved an h-index of three. His scholarly activity centers on data-driven engineering applications, particularly machine learning, construction informatics, safety analytics, and optimization. These metrics indicate an active and developing research trajectory supported by internationally accessible publications.[1]
Research Contributions
- Developed graph attention network models for spatiotemporal hazard prediction in construction safety.
- Investigated deep learning and computer vision techniques for intelligent safety management.
- Proposed machine learning frameworks for construction cost prediction and uncertainty estimation.
- Contributed hybrid reinforcement learning approaches for strategic procurement optimization.
Publications
- Meta-Analytical and Scientometric Review of Literature in Construction Engineering and Management.
- Modeling Spatiotemporal Hazard Dynamics for Construction Safety Using Graph Attention Networks.
- Improving Construction Cost Prediction and Uncertainty Quantification with a Machine Learning Imputation Framework.
- Innovations in Safety Management for Construction Sites: The Role of Deep Learning and Computer Vision Techniques.
Research Impact
Although still at an early stage of scholarly development, the research portfolio demonstrates measurable scientific visibility through citations, interdisciplinary publications, and practical engineering applications. The combination of computational intelligence, predictive modeling, and construction analytics supports broader digital transformation initiatives within infrastructure engineering and promotes reproducible analytical methodologies.[4]
Award Suitability
The Best Researcher Award recognizes sustained scholarly excellence, research quality, innovation, and disciplinary impact. Amr A. Mohy’s interdisciplinary publications demonstrate meaningful contributions to intelligent engineering systems and applied data science. His emphasis on solving real-world engineering challenges through artificial intelligence aligns with the objectives of the Computer Scientists Awards and highlights continued potential for future academic advancement.[5]
Conclusion
Amr A. Mohy’s publication record illustrates an expanding research program integrating machine learning with engineering management and construction analytics. His scholarly output contributes to safer, smarter, and more efficient engineering practices while demonstrating an evidence-based approach to scientific inquiry. Continued publication activity and collaboration are expected to strengthen both academic visibility and practical impact across data-driven engineering disciplines.
External Links
References
- Elsevier. (n.d.). Scopus author details: Amr A. Mohy, Author ID 57924030800. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57924030800
- Mohy, A. A. (2026). Meta-Analytical and Scientometric Review of Literature in Construction Engineering and Management.
https://doi.org/10.31224/7370
- Mohy, A. A. (2026). Modeling Spatiotemporal Hazard Dynamics for Construction Safety Using Graph Attention Networks.
https://doi.org/10.1108/jedt-12-2025-0720
- Mohy, A. A. (2026). Improving Construction Cost Prediction and Uncertainty Quantification with a Machine Learning Imputation Framework.
https://doi.org/10.31224/7294
- Mohy, A. A. (2026). Innovations in Safety Management for Construction Sites: The Role of Deep Learning and Computer Vision Techniques.
https://doi.org/10.1108/CI-04-2023-0062