Python-Based Algorithmic implementation of Soft Set Haussdorff Topological Spaces with an Example for Medical Diagnosis and its application in Chemical Classification
Keywords:
soft set, soft topology, soft set Haussdorff space, Bonding inert gases, bipartite graph, chemical reaction, heart diseaseAbstract
Soft set theory has demonstrated significant utility in decision-making processes involving ambiguous or imprecise data. This research presents a novel framework called SSHT-Space (Soft Set Haussdorff Topological Space), structured using a bipartite graph model. Our studies explore twodistinct SSHT-Spaces, denoted as (άS, tSS, HS) and (βS, tSS1, HS1), each designed to capture unique topological properties within the soft set context. We provide Python-based algorithmic solutions to investigate these areas, focusing on their ability to capture complicated interactions. A key application area is chemical reactions, where SSHT-Spaces represent bonding patterns, particularly among inert gases. Furthermore, we demonstrate the relevance of this approach in medical applications, particularly for understanding and predicting aspects of heart disease by employing bipartite graph structures, We improve the interpretability and practical utility of soft topological constructs in real-world scenarios, thereby opening new avenues for ongoing research and practical application.
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