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  <doi_batch_id>aspg-39-2412-1792222568</doi_batch_id>
  <timestamp>20261017073608</timestamp>
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  <journal>
   <journal_metadata language="en">
    <full_title>Neutrosophic and Information Fusion</full_title>
    <abbrev_title>NIF</abbrev_title>
    <issn media_type="electronic">2836-7863</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>2</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Efficient Data Fusion Framework for Real-Time Monitoring of Air Pressure Systems in Scania Trucks</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Durdona</given_name>
      <surname>Bakhodirova</surname>
      <affiliations>
       <institution>
        <institution_name>Department of International Business Management, Tashkent State University of Economics, Uzbekistan</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Monitoring air pressure systems in heavy-duty vehicles such as Scania trucks is a key driver for operational safety and efficiency in the automotive industry. However, the complex interaction of sensors and data sources makes it difficult to quickly detect potential system failures. This problem is solved in our paper where we present a special-purpose data fusion framework for real-time monitoring of Scania trucks’ air pressure systems. To achieve this, PCA is used to reduce the size of the dataset followed by a voting classifier which combines diverse models such as Decision Trees, Random Forests, Naive Bayes, and Linear Regression using ensemble learning. In particular, our comparative analysis shows that the Voting Classifier outperforms other ML methods in terms of prediction accuracy. These findings suggest that our fusion framework can be utilized for the early detection of air pressure anomalies in heavy-duty vehicles enhancing their safety record.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>24</first_page>
     <last_page>31</last_page>
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    <publisher_item>
     <item_number item_number_type="article-number">2412</item_number>
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     <doi>10.54216/NIF.020203</doi>
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