Some computational aspects of carbon nanocone using Q(G) operator, hexagonal network and probabilistic neural network

In this article, we first find closed forms of -polynomials of carbon nanocones using operator, hexagonal networks and probabilistic neural network. We also reckon closed forms of various degree-based topological indices of these structures. These indices are numerical tendencies that generally interprit quantitative structural activity/property/toxicity relationships and correlate certain physico-chemical properties, such as boiling point, stability, and strain energy, of respective nanomaterial.