Mohamed Afendee
Papers
1
Total Citations
10
H-Index
1
About
Mohamed Afendee’s research centers on robotics, control systems, and intelligent automation, with a particular focus on integrating adaptive neuro-fuzzy inference systems (ANFIS) and computer vision for real-world applications. His most cited work, “Design of 4 Dof Robot ARM Based on Adaptive Neuro-Fuzzy (ANFIS) using Vision in Detecting Color Objects” (2019, 10 citations), demonstrates a pioneering approach to combining Arduino-controlled 4-degree-of-freedom robotic arm mechanics with ANFIS-based decision-making. By equipping the robot with vision capabilities to detect and sort color objects, Afendee bridges the gap between traditional servo-driven hardware and adaptive, learning-based control—a contribution that enhances precision and flexibility in automated systems. His work has practical implications for manufacturing, sorting, and educational robotics, showcasing how low-cost microcontrollers can be paired with advanced algorithms to achieve intelligent behavior. While his citation count reflects a growing niche, the novelty of integrating fuzzy logic with real-time vision processing marks him as an emerging voice in adaptive robotics. Afendee’s research offers a tangible pathway for students and engineers seeking to build smarter, more responsive robots using accessible technology.
Research Focus
Key Achievements
Top Papers
- 1