Amjad Rehman
Papers
8
Total Citations
600
H-Index
7
About
Amjad Rehman is a leading researcher at the forefront of artificial intelligence, specializing in deep learning, computer vision, and intelligent systems. His work is defined by a focus on resource-conscious frameworks and innovative feature fusion strategies for complex recognition tasks. Rehman’s most impactful contributions include pioneering methods for human action recognition, where he developed a hand-crafted and deep convolutional neural network features fusion and selection strategy (154 citations) and a 26-layered deep CNN framework (105 citations). He also advanced object recognition with a sustainable deep learning model that fuses multi-layers deep features (153 citations). His influence extends to medical image analysis, evidenced by his comprehensive state-of-the-art review (79 citations), and to inclusive technology through SignExplainer, an explainable AI framework for sign language recognition using ensemble learning (57 citations). More recently, Rehman has explored 3D LiDAR point cloud segmentation for automated driving and image fusion using wavelet transformation. With over 600 total citations across his most-cited works, Amjad Rehman’s research is driving progress in autonomous systems, human-computer interaction, and accessible AI.
Research Focus
Key Achievements
Top Papers
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- 73D Lidar Point Cloud Segmentation for Automated Driving8 citations · 2024
- 8Image Fusion Using Wavelet Transformation and XGboost Algorithm5 citations · 2024