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  <doi_batch_id>aspg-18-2686-1792025623</doi_batch_id>
  <timestamp>20261015005343</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <registrant>American Scientific Publishing Group</registrant>
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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>2024</year>
    </publication_date>
    <journal_volume>
     <volume>12</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Sustainable Waste Management through ML-based Real-Time Trash Bin Prediction</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Sandeep</given_name>
      <surname>Kumar</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Vikrant</given_name>
      <surname>Shokeen</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Amit</given_name>
      <surname>Sharma</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Information Technology, IMS Engineering College, Ghaziabad, UP, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Prabhat K.</given_name>
      <surname>Srivastava</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, IMS Engineering College, Ghaziabad, UP, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Upasana</given_name>
      <surname>Dugal</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, Babu Banarasi Das University, Lucknow, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Aditi</given_name>
      <surname>Sharma</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Waste management has been an issue due to low awareness among people of any country to lead major environmental contamination, tragic accidents, and unfavorable working conditions for landfill workers. The Lack of precise and efficient object detection could be a barrier in the growth of computer vision-based systems. As per the latest research articles, pre-trained models could be used for Trash Bin detection in real time and for recommending appropriate actions after detection. Using a unique validation dataset made up of predicted trash items, the two classes of acceptable object identification models, YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector), are then contrasted. It is concluded that SSD performs noticeably better than YOLO in identifying trash objects based on several performance metrics computed utilizing multiple open-source research projects. The model is then built up to recognize several trash object types after being pre-trained using Microsoft's COCO (Common Objects in Context) dataset. Our initiative intends to enhance sustainable waste management, make trash sorting incredibly simple, and guard against serious illnesses and accidents at landfill and garbage disposal sites.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>65</first_page>
     <last_page>74</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2686</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.120205</doi>
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