Selda Bayrak
Papers
1
Total Citations
2
H-Index
1
About
Dr. Selda Bayrak is a pioneering researcher at the intersection of computer vision, assistive technology, and complex-valued neural computation. Her primary research focuses on developing advanced sign language recognition systems that bridge communication gaps for deaf and mute individuals, with a particular emphasis on American Sign Language (ASL). Dr. Bayrak’s most notable contribution is her innovative integration of Complex Zernike Moments with complex-valued deep neural networks, a novel approach that leverages the power of complex numbers—rather than traditional real-valued systems—to capture intricate spatial patterns in hand gestures. This work, published in 2024, has already garnered early citations, signaling its growing influence in the field. By moving beyond conventional real-number frameworks, her methodology enhances the accuracy and robustness of ASL recognition, offering a more nuanced understanding of gesture dynamics. Dr. Bayrak’s research not only advances machine learning theory but also has profound societal implications, aiming to facilitate smoother integration and communication for the hearing-impaired community. Her work stands as a testament to how cutting-edge computational techniques can be harnessed for inclusive technological solutions.
Research Focus
Key Achievements
Top Papers
- 1