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Automatic food bio-hazard detection system

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 3 (2023)

DOI
10.11591/ijece.v13i3.pp2652-2659
ISSN
2722-2578, 2088-8708
Published
2023-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This paper presents the design of a convolutional neural network architecture oriented to the detection of food waste, to generate a low, medium, or critical-level alarm. An architecture based on four convolution layers is used, for which a database of 100 samples is prepared. The database is used with the different hyperparameters that make up the final architecture, after the training process. By means of confusion matrix analysis, a 100% performance of the network is obtained, which delivers its output to a fuzzy system that, depending on the duration of the detection time, generates the different alarm levels associated with the risk.

Collaboration network of the authors

21 authors · 41 co-authorship links · 9 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universidad Militar Nueva Granada
  • Universidad de los Llanos
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Javier Orlando Pinzon-ArenasUniversidad Militar Nueva Granada8
César G. PachónUniversidad Militar Nueva Granada5
Luis Alfredo Rodríguez UmañaUniversidad de los Llanos5
Anny Astrid Espitia CubillosUniversidad Militar Nueva Granada3
Astrid RubianoUniversidad Militar Nueva Granada2
Walter Naranjo LouridoUniversidad de los Llanos2
Paula UsecheAffiliation unknown2
Cristian AlmanzaUniversidad de los Llanos2
Jairo David Cuero OrtegaUniversidad de los Llanos2
Juan Sebastian Sanabria JimenezUniversidad Militar Nueva Granada2
Jorge HerreraUniversidad Militar Nueva Granada2
Paula Catalina Useche-MurilloUniversidad Militar Nueva Granada2
Carlos Javier Enciso AragónUniversidad Militar Nueva Granada1
Jose Luis RamirezUniversidad Militar Nueva Granada1
Jose L. RamirezUniversidad Militar Nueva Granada1
Luis Ariel Riaño OcampoUniversidad de los Llanos1
Gustavo Andres Gallego ChipiajeUniversidad de los Llanos1
Emerson Cabanzo MosqueraUniversidad de los Llanos1
Juan C. Guachetá-AlbaUniversidad Militar Nueva Granada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Automatic food bio-hazard detection system</dc:title><dc:creator>Jimenez Moreno, Robinson</dc:creator><dc:creator>Baquero, Javier Eduardo Martinez</dc:creator><dc:description>&lt;jats:p&gt;This paper presents the design of a convolutional neural network architecture oriented to the detection of food waste, to generate a low, medium, or critical-level alarm. An architecture based on four convolution layers is used, for which a database of 100 samples is prepared. The database is used with the different hyperparameters that make up the final architecture, after the training process. By means of confusion matrix analysis, a 100% performance of the network is obtained, which delivers its output to a fuzzy system that, depending on the duration of the detection time, generates the different alarm levels associated with the risk.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v13i3.pp2652-2659</dc:identifier><dc:identifier>10.11591/ijece.v13i3.pp2652-2659</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 3 (2023); 2652</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/28759/16509</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>