Nutrition Analysis Using D_L-Nano Topological Spaces and D_R-Nano Topological Spaces through Attribute Reduction Method

K Rekha, R Maheswari

Abstract: This study offers a thoughtful and methodical application of Nano-topological concepts, grounded in rough set approximations and the theory of ideals, to address real-world decision-making challenges-in this case, nutritional health assessment. Also, this research wok formalized the structures of left and right dynamic Nano-topologies and applied them in a layered, stepwise methodology to distill the most influential nutritional factors from a set of conditional attributes. The use of equivalence relations, neighborhood analysis, and core factor extraction demonstrates a disciplined analytical framework that bridges abstract mathematical theory with tangible health-related outcomes. What stands out is the dual-stage computational process, which ensured that the core attributes-carbohydrates, calcium, proteins, and fat-emerged consistently across both topological perspectives, reinforcing their importance in sustaining well-being. This suggests that the approach has the flexibility to move beyond nutrition into other domains such as market analysis, clinical diagnostics, and academic evaluation, where complex attribute interdependencies influence decisions. By translating a mathematically sophisticated model into a tool for practical, evidence-based judgments, the work strikes a meaningful balance between theoretical elegance and applied relevance.

Keywords: Nano Topology, Attribute reduction, Ideal, D_L-Nano topological spaces, D_R-Nano topological space

How to Cite?: K Rekha, R Maheswari, "Nutrition Analysis Using D_L-Nano Topological Spaces and D_R-Nano Topological Spaces through Attribute Reduction Method", Volume 14 Issue 8, August 2025, International Journal of Science and Research (IJSR), Pages: 576-580, https://www.ijsr.net/getabstract.php?paperid=SR25811195324, DOI: https://dx.doi.org/10.21275/SR25811195324