Shams Forruque Ahmed

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About

Dr. Shams Forruque Ahmed is a rising figure in the field of multi-criteria decision-making (MCDM) and fuzzy set theory, with a particular focus on advanced mathematical frameworks for handling uncertainty. His research centers on developing novel aggregation operators and distance techniques to solve complex multiple attribute decision-making (MADM) problems, especially through the innovative use of Fermatean vague normal sets (FVNS) and Type-Ⅱ Fermatean normal numbers. Dr. Ahmed’s major contributions include pioneering logarithmic Fermatean vague normal weighted averaging operators and generalized aggregation frameworks that enhance the precision and applicability of decision models in real-world scenarios, such as robot sensor processing. His most cited work, “New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets” (2023, 14 citations), demonstrates his ability to bridge theoretical advancements with practical ranking challenges. Additionally, his paper on robot sensors (6 citations) highlights the tangible impact of his methods in engineering and automation. With a growing citation record and a focus on generalizing existing fuzzy and neutrosophic approaches, Dr. Ahmed is establishing himself as a key contributor to the evolution of intelligent decision-support systems.