Muhammad Jabir Khan
Papers
1
Total Citations
6
H-Index
1
About
Muhammad Jabir Khan is a rising researcher in the field of fuzzy set theory and its applications, with a particular focus on Pythagorean \( m \)-polar fuzzy sets. His work centers on developing novel comparison measures—including similarity, distance, and correlation coefficients—that enable more nuanced decision-making in complex, uncertain environments. Khan’s most-cited paper, “Comparison measures for Pythagorean \( m \)-polar fuzzy sets and their applications to robotics and movie recommender system” (2023, 6 citations), exemplifies his ability to bridge theoretical advances with real-world utility, demonstrating how these mathematical tools can improve path planning in robotics and enhance recommendation algorithms. Though early in his career, his contributions are already gaining traction, with his work cited by peers exploring fuzzy logic, multi-attribute decision-making, and artificial intelligence. Khan’s research is particularly notable for its interdisciplinary reach, offering practical solutions in fields ranging from molecular biology to meteorology. As he continues to refine and expand these comparison frameworks, his work promises to influence both the theoretical foundations of fuzzy mathematics and its applied impact across science and engineering.
Research Focus
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Top Papers
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