George Panoutsos

University of Sheffield, University of Leeds

Papers

Total Citations

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About

George Panoutsos is a researcher whose work spans the intersection of computational intelligence, machine learning, and real-world engineering applications. His research notably bridges advanced algorithmic methods with practical domains including agricultural robotics and human movement analysis, demonstrating a consistent commitment to translating theoretical innovation into tangible solutions. Among his most impactful contributions is his work on real-time object detection for agricultural robotics, leveraging YOLO-based deep learning architectures to automate crop identification and harvesting processes. Published in 2024, this work has already accumulated 29 citations, reflecting its immediate relevance to the growing field of agricultural automation and precision farming. Panoutsos has also made meaningful contributions to biomedical engineering, developing a combined Adaptive Neuro-Fuzzy and Bayesian framework for recognizing and predicting gait events using wearable sensors — a study that has garnered 11 citations and holds significant promise for rehabilitation engineering and assistive technologies. Across these diverse application areas, Panoutsos demonstrates expertise in hybrid intelligent systems, fuzzy logic, neural networks, and computer vision. His ability to integrate multiple computational paradigms to address complex, real-world challenges makes his research particularly valuable for students and practitioners working at the frontier of intelligent systems and autonomous technologies.