Open AccessFormation of informative signs for predicting the disease of highly productive cows with non-communicable diseases
Author(s) -
Lola Safarova
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 - 10.1088/1742-6596/1901/1/012049
Subject(s) - samara , fuzzy logic , set (abstract data type) , ketosis , disease , computer science , data mining , machine learning , medicine , artificial intelligence , endocrinology , biology , diabetes mellitus , ecology , programming language
In this article, fuzzy set membership functions have been developed based on the main factors (clinical, morphochemical, rumen contents) to predict the following non-communicable diseases such as ketosis, osteodystrophy, secondary osteodystrophy and hypomicroelementosis in high-yielding cows. The results of the study show the high efficiency of the proposed decision-making algorithm for forecasting, classifying and measuring poorly formalized processes, that are described by fuzzy models. The available knowledge about the existing experimental data makes it possible to increase the adequacy of the fuzzy expert system.