Open AccessUncertainty quantification in the fixation of Drug Dosage to cancer-induced Rats – A computational and Mathematical modeling using Fuzzy evidence theory
Author(s) -
R Judith Kiruba,
A. Deepika,
R. Ida Malarselvi,
R. Priscilla,
R. Irene Hepzibah,
C Ramachandra Raja
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1850/1/012056
Subject(s) - fuzzy logic , dempster–shafer theory , credibility , novelty , computer science , data mining , artificial intelligence , valuation (finance) , machine learning , mathematics , psychology , social psychology , finance , political science , law , economics
Results of any working out are of practical use only if statistics about their accuracy is also available. This is commonly correct in the molecular and drug design where the accuracy in each model is of vital importance. Valuation of the efficiency of changes presented in a prototypical and quantifiable evaluation of the presentation of changed models necessitates quantity for calculating the full uncertainty in designing the drug molecules, the collective impact from all improbability causes. To novelty, such a quantity is some of the points of our research. In the fuzzy model, the consequent of the fuzzy rule is often determined with degrees of belief or credibility because of vague information originating from evidence not strong enough and “lack of specificity”. In this paper, we present a fuzzy model incorporated with the fuzzy Dempster-Shafer Theory. A well-known example of drug dosage prediction is tested, the prediction results show that our fuzzy modeling is very efficient and has a strong expressive power to represent the complex system with uncertain situations.