Domestic solid waste prediction with and enhanced LSTM with SigmoReLU and RAdam optimizer

A novel approach is presented to address the prediction challenge in domestic solid waste generation through the application of machine learning techniques. To overcome the limitations inherent in capturing intricate temporal patterns faced by conventional Long Short-Term Memory (LSTM) models design...

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Bibliographic Details

Main Authors:	Mohammed Fadhel, Abdulrahman Sharaf, Ghazali, Rozaida, Md Tomari, Mohd Razali, Mohmad Hassim, Yana Mazwin, Abubakar Hassan, Abdullahi Abdi, Ismail, Lokman Hakim
Format:	Conference or Workshop Item
Language:	en
Published:	2024
Subjects:	T Technology (General)
Online Access:	http://eprints.uthm.edu.my/11973/1/Domestic%20solid%20waste%20prediction%20with%20an%20enhanced%20LSTM%20with%20SigmoRELU%20and%20RAdam%20optimizer.pdf http://eprints.uthm.edu.my/11973/ https://doi.org/10.1007/978-3-031-66965-1_26
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