  <?xml version="1.0"?>
<journal>
 <journal_metadata>
  <full_title>Fusion: Practice and Applications</full_title>
  <abbrev_title>FPA</abbrev_title>
  <issn media_type="print">2692-4048</issn>
  <issn media_type="electronic">2770-0070</issn>
  <doi_data>
   <doi>10.54216/FPA</doi>
   <resource>https://www.americaspg.com/journals/show/3873</resource>
  </doi_data>
 </journal_metadata>
 <journal_issue>
  <publication_date media_type="print">
   <year>2018</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2018</year>
  </publication_date>
 </journal_issue>
 <journal_article publication_type="full_text">
  <titles>
   <title>Enhancing Mushroom Detection Using One-Dimensional Convolutional Neural Networks</title>
  </titles>
  <contributors>
   <organization sequence="first" contributor_role="author">College of Applied Sciences, University of Technology-Iraq, 52 Alsena str., Baghdad, 10053, Iraq</organization>
   <person_name sequence="first" contributor_role="author">
    <given_name>Ahmed</given_name>
    <surname>Ahmed</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">College of Education for Humanities, University of Anbar, Ramadi, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Mustafa Muslih</given_name>
    <surname>Shwaysh</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Ministry of Higher Education and Scientific Research / Directorate of Audit and Internal Control, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Ahmed Mubdir</given_name>
    <surname>..</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Ahmed Adil</given_name>
    <surname>Nafea</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Heet Education, General Directorate of Education in Anbar, Ministry of Education, Heet, 31007 Anbar, Iraq                       </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Aythem Khairi</given_name>
    <surname>Kareem</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Biophysics, College of Applied Sciences, University of Anbar, Hit 31007, Anbar, Iraq</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Mustafa Nadhim</given_name>
    <surname>Owaid</surname>
   </person_name>
  </contributors>
  <jats:abstract xml:lang="en">
   <jats:p>The classification of mushrooms as either deadly or edible stays a important challenge due to their similar appearances, which can lead to fatal poisonings. The primary difficulty lies in identifying complex patterns in mushroom appearances, such as cap shape, color, and gill structure, which complicate accurate classification. Traditional approaches and even some machine learning (ML) models fail to capture these subtle but important distinctions, leading to misclassifications. To address this issue, this paper proposed a One-Dimensional Convolutional Neural Network (1D-CNN) approach aimed at improving the accurate of mushroom classification. By effectively recognizing complex patterns in the mushroom data set, the proposed approach greatly improves classification accuracy. The model performance evaluated utilizing Precision, Accuracy, Recall, and F1-Score that achieved high scores of 100% across all metrics. These results highlight the strength of deep learning (DL) method, specifically 1D-CNNs, in recognizing with learning complex data patterns. This shows a clear advancement over traditional ML methods and ensemble techniques, establishing the 1D-CNN as a highly reliable tool for mushroom classification that can help reduce mushroom poisoning incidents.</jats:p>
  </jats:abstract>
  <publication_date media_type="print">
   <year>2025</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2025</year>
  </publication_date>
  <pages>
   <first_page>01</first_page>
   <last_page>12</last_page>
  </pages>
  <doi_data>
   <doi>10.54216/FPA.200201</doi>
   <resource>https://www.americaspg.com/articleinfo/3/show/3873</resource>
  </doi_data>
 </journal_article>
</journal>
