Papers
2
Total Citations
16
H-Index
2
About
Khaled Chahine is a researcher at the forefront of applying artificial intelligence to solve real-world engineering challenges, with key contributions spanning renewable energy and biomedical robotics. His most impactful work, "Deep Learning Image Classification Models for Solar Panels Dust Detection" (2024, 9 citations), pioneers the use of convolutional neural networks to automatically identify dust accumulation on photovoltaic panels—a critical factor in maintaining solar energy efficiency. This research directly addresses the environmental degradation that can reduce solar panel output by up to 50%, offering a scalable, AI-driven solution for remote monitoring and maintenance. In parallel, Chahine's earlier work, "Pattern recognition of EMG signals: Towards adaptive control of robotic arms" (2016, 7 citations), demonstrates his versatility by tackling assistive technology. This study leverages electromyographic signal patterns to enable intuitive, adaptive control of prosthetic limbs, significantly improving the quality of life for amputees. By bridging deep learning with both sustainable energy and human-machine interfaces, Chahine’s research portfolio showcases a commitment to impactful, interdisciplinary innovation—using computational intelligence to enhance both environmental sustainability and human mobility.
Research Focus
Key Achievements
Top Papers
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