An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation.

Namoun, Abdallah; Hussein, Burhan Rashid; Tufail, Ali; Alrehaili, Ahmed; Syed, Toqeer Ali; BenRhouma, Oussama

Namoun, Abdallah; Hussein, Burhan Rashid; Tufail, Ali; Alrehaili, Ahmed; Syed, Toqeer Ali; BenRhouma, Oussama.

Afiliación

Namoun A; Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.
Hussein BR; School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei.
Tufail A; School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei.
Alrehaili A; Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.
Syed TA; Department of Informatics, University of Sussex, Brighton BN1 9RH, UK.
BenRhouma O; Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

Sensors (Basel) ; 22(9)2022 May 05.

Article en En | MEDLINE | ID: mdl-35591195

Asunto(s)

Residuos Sólidos; Administración de Residuos; Algoritmos; Aprendizaje Automático; Reciclaje; Residuos Sólidos/análisis

Palabras clave

IoT; ensemble learning; forecasting; machine learning; meta learner; smart cities; smart waste management; solid waste generation; time-series

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Residuos Sólidos / Administración de Residuos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza

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