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Statistical and Artificial Neural Network for the Determination of Thermo-Physical Properties of Fomoditine Drug Using Mixed Hydrotropy

C Jaya Kumar, C. Raja, V. Sampath Kumar, M. Dharmendirakumar


The experimental determination of thermo-physical properties of fomoditine drug–mixed hydrotrope concentration mixture is very time consuming as well as tedious process. Any tool helpful in estimation of these properties without experimentation can be of immense utility. In present work, other tools of determination of properties of fomoditine drug–mixed hydrotrope concentration blends were tried. A traditional statistical technique of linear regression (principle of least squares) was used to estimate the specific gravity, viscosity, surface tension and specific conductance of fomoditine drug–mixed hydrotrope concentration mixtures. A set of seven neural network architectures, three training algorithms along with ten different sets of concentration were examined to choose best Artificial Neural Network (ANN) to predict the above-mentioned properties of fomoditine drug–hydrotrope concentration mixtures. The performance of both of the traditional linear regression and ANN techniques were then compared to check their validity to predict the properties of various mixtures concentration of fomoditine drug–mixed hydrotrope concentration.

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