Research Article Open Access

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

Simanta Kumar Nayak1, Bikram Kesari Ratha2, Payodhar Padhi3, Santosh Kuamr Nanda1 and Aparajeya Panda4
  • 1 Eastern Academy of Science and Technology, India
  • 2 Utakal University, India
  • 3 Hi-Tech Medical College and Hospital, India
  • 4 Konark Institute of Science and Technology, India

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 of Computer Science
Volume 11 No. 3, 2015, 474-478

DOI: https://doi.org/10.3844/jcssp.2015.474.478

Submitted On: 24 September 2012 Published On: 1 April 2015

How to Cite: Nayak, S. K., Ratha, B. K., Padhi, P., Nanda, S. K. & Panda, A. (2015). Investigation of Quantitative Plant Activity Relationship (QPAR) for Diabetics II Using Genetic Algorithm. Journal of Computer Science, 11(3), 474-478. https://doi.org/10.3844/jcssp.2015.474.478

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Keywords

  • Herbal Drugs
  • GA