Noor Rehman

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About

Noor Rehman is a prominent figure in the rapidly evolving field of fuzzy decision science, with a core focus on advancing computational intelligence for complex real-world problems. His research primarily explores the integration of q‑rung orthopair fuzzy sets with rough set theory, creating powerful hybrid models for handling uncertainty and vagueness in multi-criteria decision-making. Rehman’s most cited work, a 2021 study on *q‑Rung Orthopair Fuzzy Rough Einstein Aggregation Information‑Based EDAS Method*, has garnered 30 citations for its novel synthesis of these mathematical frameworks. This paper not only introduced a new conceptualization of the q‑rung orthopair fuzzy rough set (q‑ROFRS) but also developed Einstein aggregation operators to enhance decision accuracy. A key achievement of this research is its practical application in robotic agrifarming, demonstrating how advanced fuzzy logic can optimize automated agricultural processes. By bridging theoretical innovation with tangible engineering challenges, Rehman’s contributions are shaping next-generation decision support systems, making him a valuable resource for students and researchers working at the intersection of fuzzy mathematics, rough sets, and intelligent automation.