Hassan Rkein
Hassan Rkein has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Alaaeddine Ramadan
College of Engineering and Computing, American University of Bahrain, Riffa, Bahrain
1 shared publication - Bassam Hamdar
Basic and Applied Sciences Research Center, Al Maaref University, Beirut P.O. Box 5078/25, Lebanon
1 shared publication - Hassan Harb
College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
1 shared publication - Kassem Danach
Affiliation of Kassem Danach: Department of Computer Science and Information Technology, Faculty of Arts and Sciences, Lebanese International University, Beirut, Lebanon
1 shared publication
Latest Publications
- Open Access
- 1 February 2026
- Published by Institute of Advanced Engineering and Science in IAES International Journal of Artificial Intelligence (IJ-AI)
- Vol. 15 (1)
AI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks
Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.
- 1 February 2026
- Published by Institute of Advanced Engineering and Science in IAES International Journal of Artificial Intelligence (IJ-AI)
- Vol. 15 (1)
Open AccessAI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks
Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.