@article {10.3844/jcssp.2015.474.478,
article_type = {journal},
title = {Investigation of Quantitative Plant Activity Relationship (QPAR) for Diabetics II Using Genetic Algorithm},
author = {Nayak, Simanta Kumar and Ratha, Bikram Kesari and Padhi, Payodhar and Nanda, Santosh Kuamr and Panda, Aparajeya},
volume = {11},
number = {3},
year = {2015},
month = {Apr},
pages = {474-478},
doi = {10.3844/jcssp.2015.474.478},
url = {https://thescipub.com/abstract/jcssp.2015.474.478},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}