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About

Madhumangal Pal is a prominent researcher working at the intersection of fuzzy mathematics, graph theory, and intelligent decision-making systems. His scholarship is distinguished by sophisticated contributions to advanced fuzzy set frameworks, including Pythagorean vague normal sets, neutrosophic normal sets, m-polar fuzzy graphs, and q-rung orthopair fuzzy environments — mathematical structures designed to handle uncertainty and imprecision in complex real-world systems. Pal's most recognized contributions center on developing novel multiple-attribute decision-making (MADM) models that leverage these rich mathematical frameworks to solve applied problems in robotics manufacturing, surgical systems, medical robot selection, and agricultural automation. His 2023 work on m-polar fuzzy graphs introduced the concept of inverse graphs within this environment, extending classical crisp graph theory into a nuanced multi-component relational setting with promising robotics allocation applications. Similarly, his research on logarithmic square root neutrosophic normal sets offers innovative aggregation operators for engineering selection challenges. With recent papers accumulating citations across robotics, medical engineering, and agricultural decision systems, Pal's work is gaining steady recognition within the fuzzy mathematics and computational intelligence communities. His research provides students and practitioners with powerful quantitative tools for navigating uncertainty across high-stakes technological domains.