Prikaz osnovnih podataka o dokumentu

dc.contributor.authorRandjelovic, Branislav M.
dc.contributor.authorMitic, Vojislav V.
dc.contributor.authorRibar, Srdjan
dc.contributor.authorMilosevic, Dusan M.
dc.contributor.authorLazovic, Goran
dc.contributor.authorFecht, Hans J.
dc.contributor.authorVlahovic, Branislav
dc.date.accessioned2022-11-05T08:53:34Z
dc.date.available2022-11-05T08:53:34Z
dc.date.issued2022
dc.identifier.citationИИИ 43007 “Истраживање климатских промена и њиховог утицаја на животну средину - праћење утицаја, адаптација и ублажавање”en_US
dc.identifier.citationТР 32012 „Интелигентни Кабинет за Физикалну Медицину – ИКАФИМ“en_US
dc.identifier.urihttps://platon.pr.ac.rs/handle/123456789/917
dc.description.abstractMany recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful. Theory of fractals, artificial neural networks and graph theory are most commonly used in such papers. These methods are useful tools for applying mathematics in nanostructures, especially given the diversity of the methods, as well as their compatibility and complementarity. The purpose of this paper is to provide an overview of existing results in the field of electrochemical and magnetic nanostructures parameter modeling by applying the three methods that are “easy to use”: theory of fractals, artificial neural networks and graph theory. We also give some new conclusions about applicability, advantages and disadvantages in various different circumstances.en_US
dc.language.isoen_USen_US
dc.publisherМДПИ Базел, Швајцарскаen_US
dc.rightsCC0 1.0 Универзална*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.titleFractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterizationen_US
dc.title.alternativeFractal and Fractionalen_US
dc.typeclanak-u-casopisuen_US
dc.description.versionpublishedVersionen_US
dc.identifier.doihttps://doi.org/10.3390/fractalfract6030134
dc.citation.volume6
dc.citation.spage134
dc.subject.keywordsfractals, artificial neural networks, graph theory, materialsen_US
dc.type.mCategoryM21en_US
dc.type.mCategoryopenAccessen_US
dc.type.mCategoryM21en_US
dc.type.mCategoryopenAccessen_US
dc.identifier.ISSNEISSN 2504-3110


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