@inproceedings{323e31ddeca74dc68ca235f69443cd5f,
title = "Weight Prediction of a Beehive Using Bi-LSTM Network",
abstract = "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 W{\"u}rzburg, 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.",
keywords = "Beehive, Bi-LSTM network, TensorFlow, Weight prediction",
author = "Salas, {Mar{\'i}a Celeste} and Hernando Gonz{\'a}lez and Hern{\'a}n Gonz{\'a}lez and Carlos Arizmendi and Alhim Vera",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Information Technology and Systems, ICITS 2023 ; Conference date: 24-04-2023 Through 26-04-2023",
year = "2023",
doi = "10.1007/978-3-031-33258-6_27",
language = "Ingl{\'e}s",
isbn = "9783031332579",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "285--295",
editor = "{\'A}lvaro Rocha and Carlos Ferr{\'a}s and Waldo Ibarra",
booktitle = "Information Technology and Systems - ICITS 2023",
}