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  <doi_batch_id>aspg-18-1851-1792025641</doi_batch_id>
  <timestamp>20261015005401</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>9</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Intelligent Multi-Level Feature Fusion Using Remote Sensing and CNN Image Classification Algorithm</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Mustafa</given_name>
      <surname>Altaee</surname>
      <affiliations>
       <institution>
        <institution_name>Department of medical instruments engineering techniques, Alfarahidi University, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Talib</given_name>
      <surname>A.</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Communications Engineering Department, National University of science and technology , Thi Qar, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>M. A.</given_name>
      <surname>Jalil</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Engineering techniques, Alturath University college, Baghdad, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ali</given_name>
      <surname>J.</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Engineering techniques, Mazaya University college, Thi Qar, Iraq ; MEU Research Unit, Middle East University, Amman 11831, Jordan</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Thamer A.</given_name>
      <surname>Alalwani</surname>
      <affiliations>
       <institution>
        <institution_name>Radiological Techniques Department, Al- Mustaqbal University College, 51001 Hilla, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The collection of fetures in both multispectral and hyperspectral domains is possible with Hyperspectral Image (HSI). It comprises a vast array of multispectral bands with functional relationships. However, they become more complex when dealing with small samples. To tackle this issue, researchers employed a deep learning convolutionary neural network system (DL-CNN) and implemented a temporal abstraction strategy to grade HSI. This approach is an intelligent multi-level feature fusion that combines the temporal abstraction strategy and DL-CNN for HSI grading. Researchers have introduced the impact of seven separate classifiers in implementing the Location estimation on our broad CNN framework, which plays the shallow CNN model's main training phase. PSO, Adagrad, Plans to implement, Alexnet, Adam, Environmental benefits, and Nadam are the seven distinct remained significantly included in this analysis. A detailed study of the four multispectral remote sensing techniques sets showed the deep CNN system's supremacy followed with the HSI identification AlexNet Optimizer.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>36</first_page>
     <last_page>48</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1851</item_number>
    </publisher_item>
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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/JISIoT.090103</doi>
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