@inproceedings{7c7942c6bf32468dad7ddcfea7d27b58,
title = "Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning",
abstract = "Internet of Things (IoT) technologies have been proliferated as a solution able to deliver on the promise of efficient waste management. Their application has been explored in various stages of the waste management process leading to significant improvements compared to conventional methods. Machine learning techniques for IoT sensor data analysis make the implementation of smart waste management systems feasible and a offer a valuable tool to municipal authorities in the challenging process of waste management. This paper applies a set of machine learning regression models and a soft voting meta ensemble for predicting the cost of waste management using an open online dataset. Our study reveals that the CatBoost Regressor performs better than the rest of the models tested with the meta ensemble slightly improving its performance.",
keywords = "Cost Prediction, Internet of Things, Machine Learning, Waste Management",
author = "Gregory Davrazos and Theodor Panagiotakopoulos and Sotiris Kotsiantis and Achilles Kameas",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023 ; Conference date: 10-07-2023 Through 12-07-2023",
year = "2023",
doi = "10.1109/IISA59645.2023.10345856",
language = "English",
series = "14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023",
}
