Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/90421
Title: Using neural network for simulations to improve the quality of disease diagnosis: Technical aspects
Authors: Gilev, D. V.
Loginovsky, O. V.
Issue Date: 2020
Publisher: World Academy of Research in Science and Engineering
Citation: Gilev, D. V. Using neural network for simulations to improve the quality of disease diagnosis: Technical aspects / D. V. Gilev, O. V. Loginovsky. — DOI 10.30534/ijatcse/2020/289942020 // International Journal of Advanced Trends in Computer Science and Engineering. — 2020. — Vol. 4. — Iss. 9. — P. 6156-6159.
Abstract: Mathematical models are important for the processes of cognition and decision-making. They provide a concise representation of significant relationships in the description of objects and situations. Adding new relationships leads to narrowing the scope of applicability of the model. The formula is an example of a compressed description of a potentially infinite set of objects and situations. Knowledge processing is based on the use of mathematical methods. In this case, it is the most thorough, at least from the point of view of strict logic and consistent formalization. To process knowledge, we must present it in some form that is convenient for analysis. Thus, when analyzing data and knowledge, we do not use them directly, but their representations. Mathematical models of objects and phenomena are an effective way of representation. This is now the most powerful method of cognition of processes, objects and phenomena. Modeling is a special way of scientific research. A mathematical model of an object is a mathematical structure interpreted within a given domain. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
Keywords: IMPROVE
NEURAL NETWORK
QUALITY
SIMULATIONS
URI: http://hdl.handle.net/10995/90421
https://elar.urfu.ru/handle/10995/90421
metadata.dc.rights: info:eu-repo/semantics/openAccess
SCOPUS ID: 85090296552
PURE ID: 13918456
ISSN: 2278-3091
DOI: 10.30534/ijatcse/2020/289942020
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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