Mouna Afif
Papers
4
Total Citations
149
H-Index
4
About
Mouna Afif is a researcher at the forefront of applying deep learning to computer vision, with a particular focus on indoor scene understanding and assistive robotics. Her work bridges the gap between artificial intelligence and practical applications, from smart navigation systems to medical imaging. Afif’s most influential contribution is her development of deep learning frameworks for indoor image recognition, as demonstrated by her highly cited 2020 paper “Deep Learning Based Application for Indoor Scene Recognition” (76 citations). She has further advanced the field through her evaluation of EfficientDet for object detection in indoor robot assistance navigation (25 citations), showcasing her commitment to real-world robotic applications. Her research extends to medical imaging, where she has explored transfer deep learning for ultrasonic computed tomographic image classification. With a growing citation impact, Afif’s work is shaping how machines perceive and interact with complex indoor environments, making her a key figure in the intersection of deep learning, robotics, and medical technology.
Research Focus
Key Achievements
Top Papers
- 1Deep Learning Based Application for Indoor Scene Recognition76 citations · 2020
- 2
- 3
- 4