WEEE Prediction Model Based on Neural Networks

Autores
Facuy, Jussen; Pasini, Ariel Cristian; Estévez, Elsa Clara; Moran, César
Año de publicación
2025
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The need to develop intelligent and innovative solutions to reduce pollution generated by Waste Electrical and Electronic Equipment (WEEE) led to the construction of a WEEE prediction model based on neural networks. The information supporting the model is derived from data obtained through a survey, as well as historical data on WEEE generation in Ecuador. The model aims to estimate waste generation within a specific month and year. Neural network algorithms were used for the model's functionality due to their adaptability to dynamic data like the ones utilized. The development of this model considered five phases: data collection, preprocessing, model generation, model application, and verification and continuous improvement. It is concluded that the proposed model provides a detailed description of the architecture, phases, and procedures required for its operation, facilitating its understanding and subsequent implementation.
Instituto de Investigación en Informática
Materia
Ciencias Informáticas
Prediction Model
Neural Networks
WEEE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/182608
Acceder
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network_name_str SEDICI (UNLP)
spelling WEEE Prediction Model Based on Neural NetworksFacuy, JussenPasini, Ariel CristianEstévez, Elsa ClaraMoran, CésarCiencias InformáticasPrediction ModelNeural NetworksWEEEThe need to develop intelligent and innovative solutions to reduce pollution generated by Waste Electrical and Electronic Equipment (WEEE) led to the construction of a WEEE prediction model based on neural networks. The information supporting the model is derived from data obtained through a survey, as well as historical data on WEEE generation in Ecuador. The model aims to estimate waste generation within a specific month and year. Neural network algorithms were used for the model's functionality due to their adaptability to dynamic data like the ones utilized. The development of this model considered five phases: data collection, preprocessing, model generation, model application, and verification and continuous improvement. It is concluded that the proposed model provides a detailed description of the architecture, phases, and procedures required for its operation, facilitating its understanding and subsequent implementation.Instituto de Investigación en Informática2025-06info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf63-73http://sedici.unlp.edu.ar/handle/10915/182608enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2583-1info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:41:39Zoai:sedici.unlp.edu.ar:10915/182608Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:41:39.873SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv WEEE Prediction Model Based on Neural Networks
title WEEE Prediction Model Based on Neural Networks
spellingShingle WEEE Prediction Model Based on Neural Networks
Facuy, Jussen
Ciencias Informáticas
Prediction Model
Neural Networks
WEEE
title_short WEEE Prediction Model Based on Neural Networks
title_full WEEE Prediction Model Based on Neural Networks
title_fullStr WEEE Prediction Model Based on Neural Networks
title_full_unstemmed WEEE Prediction Model Based on Neural Networks
title_sort WEEE Prediction Model Based on Neural Networks
dc.creator.none.fl_str_mv Facuy, Jussen
Pasini, Ariel Cristian
Estévez, Elsa Clara
Moran, César
author Facuy, Jussen
author_facet Facuy, Jussen
Pasini, Ariel Cristian
Estévez, Elsa Clara
Moran, César
author_role author
author2 Pasini, Ariel Cristian
Estévez, Elsa Clara
Moran, César
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Prediction Model
Neural Networks
WEEE
topic Ciencias Informáticas
Prediction Model
Neural Networks
WEEE
dc.description.none.fl_txt_mv The need to develop intelligent and innovative solutions to reduce pollution generated by Waste Electrical and Electronic Equipment (WEEE) led to the construction of a WEEE prediction model based on neural networks. The information supporting the model is derived from data obtained through a survey, as well as historical data on WEEE generation in Ecuador. The model aims to estimate waste generation within a specific month and year. Neural network algorithms were used for the model's functionality due to their adaptability to dynamic data like the ones utilized. The development of this model considered five phases: data collection, preprocessing, model generation, model application, and verification and continuous improvement. It is concluded that the proposed model provides a detailed description of the architecture, phases, and procedures required for its operation, facilitating its understanding and subsequent implementation.
Instituto de Investigación en Informática
description The need to develop intelligent and innovative solutions to reduce pollution generated by Waste Electrical and Electronic Equipment (WEEE) led to the construction of a WEEE prediction model based on neural networks. The information supporting the model is derived from data obtained through a survey, as well as historical data on WEEE generation in Ecuador. The model aims to estimate waste generation within a specific month and year. Neural network algorithms were used for the model's functionality due to their adaptability to dynamic data like the ones utilized. The development of this model considered five phases: data collection, preprocessing, model generation, model application, and verification and continuous improvement. It is concluded that the proposed model provides a detailed description of the architecture, phases, and procedures required for its operation, facilitating its understanding and subsequent implementation.
publishDate 2025
dc.date.none.fl_str_mv 2025-06
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/182608
url http://sedici.unlp.edu.ar/handle/10915/182608
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2583-1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.format.none.fl_str_mv application/pdf
63-73
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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