Affiliations: [a]
School of Computer Science, Chengdu University, Chengdu, China
| [b]
Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Pakistan
| [c]
Department of Mathematics, University of Management and Technology, Lahore, Pakistan
| [d]
Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan
Correspondence:
[*]
Corresponding author. Muhammad Farhan Hanif. E-mail: [email protected].
Abstract: The triangular γ-graphyne structure is highlighted in particular, as it is a new configuration with possible applications in medicine. We shed light on this structure’s special qualities and potential uses in healthcare by computing several topological indices linked to it through computational research. Furthermore, we use Shannon’s entropy measure to express the information content of the connection-based topological indices in tandem. This method offers a thorough comprehension of the intricate features and structural properties of the triangular γ-graphyne structure. A logarithmic regression model is built to establish a quantifiable relationship between the computed indices and entropy. The SPSS program was used in the development of this model, allowing for a thorough examination of the relationship between structural features and informational entropy. A regression model based on triangular graphyne topological indices is used as a predictive tool for entropy estimation.
Keywords: Connection number (CN), triangular γ-graphyne, line graph, logarithmic regression model, Shannon entropy
