IMPROVEMENT OF THE DIAGNOSIS ALGORITHM BASED ON ARTIFICIAL TESTING DATA
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Abstract
Today, the construction of diagnostic models based on the theory of fuzzy logic algorithms and neutrosophic fuzzy sets, the classification of poorly formed processes is a developing direction. However, the diagnosis and analysis of the causes of the spread of animal disease with the introduction of modern information technologies require further improvement. The practical significance of the research results lies in the creation of software for practical tasks of assessing and predicting the state of intelligent systems with fuzzy initial information. A database of symptoms of diseases of microelementosis, ketosis, osteodystrophy, secondary osteodystrophy in cattle has been formed and systematically formalized, and a decision support program has been created to help veterinarians to assess the condition of cattle. The developed algorithms and software allow to increase the economic efficiency of diagnosis and prognosis of the disease based on the statistical data of the regional department of veterinary medicine and livestock development.
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