Saleem Abdullah
Papers
9
Total Citations
207
H-Index
5
About
Saleem Abdullah is a prominent researcher specializing in fuzzy set theory, multi-attribute group decision making (MAGDM), and intelligent decision support systems. His work centers on developing advanced mathematical frameworks to handle uncertainty and imprecision in complex real-world scenarios, with particular emphasis on robot selection and industrial applications. Abdullah has made significant contributions to the extension and application of fuzzy set structures. His highly cited 2019 paper introducing a fuzzy TOPSIS method under spherical fuzzy information (94 citations) advanced the field by accommodating greater uncertainty than traditional fuzzy frameworks. Complementing this, his work on 2-tuple picture fuzzy linguistic aggregation operators and neural network approaches via double hierarchy linguistic information (76 citations) demonstrated innovative pathways for tackling sophisticated decision-making problems. Throughout his career, Abdullah has consistently bridged theoretical mathematics with practical applications, addressing challenges in robotics, agriculture automation, optical manufacturing, and smart industries. His research portfolio spans neutrosophic sets, bipolar aggregation operators, intuitionistic fuzzy rough TOPSIS, and fractional fuzzy sets, reflecting both depth and breadth. With papers accumulating over 200 citations collectively, his scholarship continues influencing researchers navigating uncertainty modeling and computational intelligence, making him a valuable reference for students exploring fuzzy mathematics and decision science.
Research Focus
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