Lina Elmoiz Alatabani
Papers
1
Total Citations
11
H-Index
1
About
Lina Elmoiz Alatabani is a rising researcher at the intersection of artificial intelligence and robotics, whose work focuses on harnessing machine learning and deep learning to advance autonomous systems. Her most cited paper, "Machine Learning and Deep Learning Approaches for Robotics Applications" (2023), has already garnered 11 citations, reflecting its timely synthesis of cutting-edge techniques for perception, control, and decision-making in robotic platforms. Alatabani’s contributions lie in bridging theoretical models with practical implementations, offering frameworks that improve robot adaptability in dynamic environments. Her research addresses key challenges such as sensor fusion, path planning, and human-robot interaction, making her work valuable for both academic and industrial applications. By integrating deep learning architectures like convolutional and recurrent neural networks into robotic systems, she has helped push the boundaries of what machines can learn and execute autonomously. Alatabani’s growing citation record signals her emerging influence, and her clear, application-driven approach makes her a promising voice for students and engineers seeking to understand how AI can transform robotics from lab experiments into real-world solutions.
Research Focus
Key Achievements
Top Papers
- 1Machine Learning and Deep Learning Approaches for Robotics Applications11 citations · 2023