Abdelkader Benyettou

Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf

Papers

Total Citations

H-Index

About

Abdelkader Benyettou is a researcher whose work bridges artificial intelligence, robotics, and biomedical applications. His key research areas include neural network-based navigation systems, immune-inspired algorithms, and pattern recognition for medical diagnostics. One of his most notable contributions is the development of an immune-inspired approach for breast cancer classification, published in 2013, which has garnered 9 citations and demonstrates the potential of bio-computing methods in healthcare. In robotics, Benyettou advanced reactive navigation by introducing a temporal radial basis function (TRBF) approach for mobile robots, enabling autonomous movement in structured indoor environments. This work, published in 2004, laid groundwork for neural network-driven robotic control. While his citation counts reflect focused, specialized impact, his interdisciplinary approach—combining computational intelligence with real-world applications—highlights his dedication to solving practical problems. Benyettou’s research offers valuable insights for students and researchers exploring the intersection of machine learning, robotics, and medical informatics, particularly in developing adaptive systems for complex environments.