Majed Alhaisoni

Papers

Total Citations

204

H-Index

About

Majed Alhaisoni is a leading researcher in the fields of computer vision and deep learning, with a specific focus on sustainable and efficient object recognition and human action recognition. His most impactful work, “A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection” (2020), has garnered 153 citations, establishing a foundational approach for enhancing the accuracy and robustness of autonomous systems in intelligent robotics and visual surveillance. Alhaisoni’s key contribution lies in pioneering multi-layered deep features fusion techniques, which intelligently combine and select features from different neural network layers to improve performance without excessive computational cost. This work is further extended in his 2021 paper on human action recognition (51 citations), where he applies similar fusion strategies to advance applications in visual surveillance and pedestrian detection. By addressing the critical challenge of maintaining system performance under varying object conditions, Alhaisoni has significantly influenced the development of more reliable and adaptable AI-driven vision systems, making his research essential for students and engineers working on next-generation autonomous technologies.