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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/182608
Ver los metadatos del registro completo
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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:UNLP2025-09-29T11:49:52Zoai: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:13292025-09-29 11:49:53.025SEDICI (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 |
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conferenceObject |
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publishedVersion |
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eng |
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eng |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) |
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