On Curvilinear Regression Analysis via Newly Proposed Entropies for Some Benzene Models - Guangwu Liu; Muhammad Kamran Siddiqui; Shazia Manzoor; Muhammad Naeem; Douhadji Abalo (Complexity)
To avoid exorbitant and extensive laboratory experiments, QSPR analysis, based on topological descriptors, is a very constructive statistical approach for analyzing the numerous physical and chemical properties of compounds. Therefore, we presented some new entropy measures which are based on the sum of the neighborhood degree of the vertices. Firstly, we made the partition of the edges of benzene derivatives which are based on the degree sum of neighboring vertices and then computed the neighborhood version of entropies. Secondly, we made use of the software SPSS for developing a correlation between newly introduced entropies and the physicochemical properties of benzene derivatives. Our obtained results demonstrated that the critical temperature CT, critical pressure CP, and critical volume CV can be predicted through fifth geometric arithmetic entropy, second SK entropy, and fifth ND entropy, respectively. Other remaining physical characteristics include Gibb’s energy qℰ, logP, molar refractivity ℳℛ, and Henry’s law ℋℒ that can be predicted by using sixth ND entropy.