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  <doi_batch_id>aspg-3-822-1791417681</doi_batch_id>
  <timestamp>20261008000121</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Fusion: Practice and Applications</full_title>
    <abbrev_title>FPA</abbrev_title>
    <issn media_type="print">2770-0070</issn>
    <issn media_type="electronic">2692-4048</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2021</year>
    </publication_date>
    <journal_volume>
     <volume>6</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>A Hybrid Approach for Neural Network in Pattern Storage</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Kumud</given_name>
      <surname>Sachdeva</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Shruti</given_name>
      <surname>Aggarwal</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Your mind does not manufacture your mind. Your mind forms neural networks. Neural networks had been effectively carried out to numerous sample garage and type troubles in phrases in their mastering ability, excessive discrimination electricity, and exceptional generalization ability. The achievement of many mastering schemes isn't always assured, however, seeing that algorithms like backpropagation have many drawbacks like stepping into the nearby minima, for that reason imparting suboptimal solutions. In the case of classifying big sets and complicated patterns, the traditional neural networks are afflicted by numerous problems inclusive of the dedication of the shape and length of the network, the computational complexity, and so on. This paper introduces neural computing techniques especially radial foundation features network. Various upgrades and trends made in an artificial neural network for rushing up training, keeping off nighborhood minima, growing the generalization capacity and different capabilities are reviewed.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2021</year>
    </publication_date>
    <pages>
     <first_page>43</first_page>
     <last_page>49</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">822</item_number>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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    <doi_data>
     <doi>10.54216/FPA.060201</doi>
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       <resource mime_type="application/pdf">https://www.americaspg.com/storage/524.pdf</resource>
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