| Issue |
E3S Web of Conf.
Volume 458, 2023
International Scientific Conference Energy Management of Municipal Facilities and Environmental Technologies (EMMFT-2023)
|
|
|---|---|---|
| Article Number | 09023 | |
| Number of page(s) | 5 | |
| Section | IT and Mathematical Modeling in Energy Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202345809023 | |
| Published online | 07 December 2023 | |
Algebraic reconfiguration of LSTM network for automated video data stream analytics using applied machine learning
T.F. Gorbachev Kuzbass State Technical University, 650000, Kemerovo, 28 Vesennya st., Russian Federation
* Corresponding author: pylovpa@kuzstu.ru
Recurrent neural networks (RNNs) are a powerful tool for processing sequential data. However, the standard LSTM architecture, despite its effectiveness in capturing long-range dependencies, can still encounter some problems when dealing with particularly complex sequences. In this paper, we present a mathematical modification of LSTM that extends the basic advantages of the long short-term memory model and will help to model complex dependencies in data more accurately.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.