Pervez Khan
Papers
1
Total Citations
58
H-Index
1
About
Dr. Pervez Khan is a leading researcher in the integration of artificial intelligence, natural language processing, and social robotics. His work focuses on enabling more intuitive human-robot interaction by merging semantic technologies with machine learning. His most cited paper, "Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots" (2017, 58 citations), addresses a critical challenge: how social robots can intelligently interpret spoken queries and deliver accurate, context-aware recommendations. By combining ontology-based knowledge representation with Support Vector Machine (SVM) classifiers, Dr. Khan’s system allows robots to move beyond simple keyword matching, enabling them to extract meaningful information and provide personalized suggestions. This foundational work has influenced subsequent research in human-robot communication and intelligent recommendation systems. Dr. Khan’s contributions are particularly valuable for advancing the capabilities of social robots in real-world applications, from assistive technologies to interactive service robots. His research continues to shape how machines understand and respond to human language, making him a notable figure in the field of socially intelligent robotics.
Research Focus
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Top Papers
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