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            <front>

                <journal-meta>
                                                                <journal-id>j. inst. sci. and tech.</journal-id>
            <journal-title-group>
                                                                                    <journal-title>Journal of the Institute of Science and Technology</journal-title>
            </journal-title-group>
                                        <issn pub-type="epub">2536-4618</issn>
                                                                                            <publisher>
                    <publisher-name>Igdir University</publisher-name>
                </publisher>
                    </journal-meta>
                <article-meta>
                                        <article-id pub-id-type="doi">10.21597/jist.1230287</article-id>
                                                                <article-categories>
                                            <subj-group  xml:lang="en">
                                                            <subject>Mathematical Sciences</subject>
                                                    </subj-group>
                                            <subj-group  xml:lang="tr">
                                                            <subject>Matematik</subject>
                                                    </subj-group>
                                    </article-categories>
                                                                                                                                                        <title-group>
                                                                                                                        <article-title>Investigation of Solutions of 𝜷 −conformable Fractional Ordinary Differential Equation With  Artificial Neural Network</article-title>
                                                                                                                                        </title-group>
            
                                                    <contrib-group content-type="authors">
                                                                        <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0001-5026-6534</contrib-id>
                                                                <name>
                                    <surname>Bulut</surname>
                                    <given-names>Sadullah</given-names>
                                </name>
                                                                    <aff>ERZURUM TECHNICAL UNIVERSITY</aff>
                                                            </contrib>
                                                    <contrib contrib-type="author">
                                                                    <contrib-id contrib-id-type="orcid">
                                        https://orcid.org/0000-0003-4255-5760</contrib-id>
                                                                <name>
                                    <surname>Yiğider</surname>
                                    <given-names>Muhammed</given-names>
                                </name>
                                                                    <aff>ERZURUM TECHNICAL UNIVERSITY</aff>
                                                            </contrib>
                                                                                </contrib-group>
                        
                                        <pub-date pub-type="pub" iso-8601-date="20230601">
                    <day>06</day>
                    <month>01</month>
                    <year>2023</year>
                </pub-date>
                                        <volume>13</volume>
                                        <issue>2</issue>
                                        <fpage>1266</fpage>
                                        <lpage>1274</lpage>
                        
                        <history>
                                    <date date-type="received" iso-8601-date="20230106">
                        <day>01</day>
                        <month>06</month>
                        <year>2023</year>
                    </date>
                                                    <date date-type="accepted" iso-8601-date="20230223">
                        <day>02</day>
                        <month>23</month>
                        <year>2023</year>
                    </date>
                            </history>
                                        <permissions>
                    <copyright-statement>Copyright © 2011, Journal of the Institute of Science and Technology</copyright-statement>
                    <copyright-year>2011</copyright-year>
                    <copyright-holder>Journal of the Institute of Science and Technology</copyright-holder>
                </permissions>
            
                                                                                                <abstract><p>İn this study, we present a method in order to get initial value fractional differential equations with artificial neural networks. On the basis of the function approach of feedforward neural networks, this method is a general method that is written in an implicit analytical form and results in the creation of a differentiable solution. The first part of the created trial solution which is stated as the sum of the two parts, with no controllable parameters, gives the initial conditions. The second part, unaffected by the initial conditions, consists of a feedforward neural network with controllable parameters (weights). The applicability of this approach is demonstrated in systems of both fractional single ODEs and fractional coupled ODEs.</p></abstract>
                                                                                    
            
                                                            <kwd-group>
                                                    <kwd>•	NeuralNetwork Method</kwd>
                                                    <kwd>  •	Numerical Solutions</kwd>
                                                    <kwd>  •	Fractional 
Diﬀerential Equations</kwd>
                                            </kwd-group>
                                                        
                                                                            <kwd-group xml:lang="tr">
                                                    <kwd>•	NeuralNetwork Method</kwd>
                                                    <kwd>  •	Numerical Solutions</kwd>
                                                    <kwd>  •	Fractional 
Diﬀerential Equations</kwd>
                                            </kwd-group>
                                                                                                            </article-meta>
    </front>
    <back>
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