Litcius/Paper detail

Exploring Entropy Measures with Topological Indices on Subdivided Cage Networks via Linear Regression Analysis

Rongbing Huang, Muhammad Farhan Hanif, Muhammad Faisal Hanif, Muhammad Kamran Siddiqui, Mazhar Hussain, Eihab B. M. Bashier

2024Applied Artificial Intelligence13 citationsDOIOpen Access PDF

Abstract

In this study, we investigate entropy measurements for subdivided cage networks based on topological indices. We specifically calculate different entropy, redefining Zagreb entropy, HM(G),M1(G), M2(G) entropy, atom bond connection entropy, and Randic entropy. We examine the graphical behavior of various entropy measures using the line fit approach. The results highlight patterns in the distribution of entropy values and interactions between them, which shed light on the intricate connectivity and structural properties of segmented cage networks. This work improves our understanding of cage network dynamics and provides a visual framework for interpreting their behavior.

Topics & Concepts

Computer scienceLinear regressionEntropy (arrow of time)RegressionCageArtificial intelligenceData miningMachine learningTopology (electrical circuits)Theoretical computer scienceAlgorithmStatisticsMathematicsCombinatoricsThermodynamicsPhysicsGraph theory and applicationsTopological and Geometric Data AnalysisComputational Drug Discovery Methods