Weight Prediction of a Beehive Using Bi-LSTM Network

María Celeste Salas, Hernando González, Hernán González, Carlos Arizmendi, Alhim Vera

Producción científica: Libro / Capitulo del libro / InformeLibros de Investigaciónrevisión exhaustiva

Resumen

Predicting the future weight of an artificial beehive is fundamental to determine the status and production of an artificial bee beehive, the more weight the beehive has at harvest times the more productive it will be, whether the weight was increased by honey, propolis, royal jelly or brood. This paper presents a bidirectional algorithm (Bi-LSTM) using different configurations and activation functions to obtain different results in order to determine the most accurate prediction for future beehive weight. The models were implemented on a database of an artificial beehive obtained from Kaggle.com, whose location of the beehive is in Würzburg, Germany; the data were taken for the whole year 2017 and the variables obtained from the database are humidity and temperature inside the beehive and beehive weight.

Idioma originalInglés
Título de la publicación alojadaInformation Technology and Systems - ICITS 2023
EditoresÁlvaro Rocha, Carlos Ferrás, Waldo Ibarra
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas285-295
Número de páginas11
ISBN (versión impresa)9783031332579
DOI
EstadoPublicada - 2023
EventoInternational Conference on Information Technology and Systems, ICITS 2023 - Cusco, Perú
Duración: 24 abr. 202326 abr. 2023

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen691 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

ConferenciaInternational Conference on Information Technology and Systems, ICITS 2023
País/TerritorioPerú
CiudadCusco
Período24/04/2326/04/23

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