Abdel‐Hamid Soliman

Papers

Total Citations

H-Index

About

Abdel-Hamid Soliman is a researcher at the intersection of agricultural robotics and cloud-native computing, making notable contributions in both precision agriculture and container orchestration. His most cited work, "A Real-Time Olive Fruit Detection for Harvesting Robot Based on YOLO Algorithms" (2023, 23 citations), demonstrates his expertise in applying deep learning to agricultural automation. This paper explores state-of-the-art object detection frameworks for fruit recognition, addressing the critical challenge of enabling harvesting robots to identify and locate olives in real-time—a key step toward autonomous crop collection. In parallel, Soliman has advanced cloud infrastructure efficiency through his work on "Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative Framework" (2022, 8 citations). This research tackles the complexities of resource management in containerized environments, specifically optimizing Vertical Pod Autoscaler (VPA) mechanisms to improve elastic scaling alongside traditional Horizontal Pod Autoscalers. By bridging the gap between AI-driven agricultural solutions and scalable cloud computing systems, Soliman’s work offers practical pathways for deploying intelligent, resource-efficient technologies in both field and data center contexts.