Journal of Computer Science

Investigation of Quantitative Plant Activity Relationship (QPAR) for Diabetics II Using Genetic Algorithm

Simanta Kumar Nayak, Bikram Kesari Ratha, Payodhar Padhi, Santosh Kuamr Nanda and Aparajeya Panda

DOI : 10.3844/jcssp.2015.474.478

Journal of Computer Science

Volume 11, Issue 3

Pages 474-478

Abstract

The present study demonstrates a novel computational approach for Indian Traditional Medicine (ITM) for the effective antidiabetic drug. Indian traditional practitioners are using many natural Herbals for the cure of diabetes. Though many diabetes patients are getting temporarily cured still its cause and effects are unknown due to lack of proper scientific investigation. Individual plant bioactivities have been already investigated by many researchers but the combined plant bioactivity effects have not been studied yet because it requires more number of experiments which is time consuming and expensive. Regular diabetic medicines available in the market still not based on the optimal plant bioactivity database and as a result of which the effectiveness of the medicine also reduced. To overcome the above drawback a novel computational approach was proposed for multiple antidiabetic plants in appropriate proportions for its optimization. Since the process is stochastic in nature Genetic Algorithm (GA) tool was selected for the design. The actual and predicted results have been compared in this study.

Copyright

© 2015 Simanta Kumar Nayak, Bikram Kesari Ratha, Payodhar Padhi, Santosh Kuamr Nanda and Aparajeya Panda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.