LSTM networks are successfully employed in satellite ground stations to predict mechanical failures in antenna servo systems. This enables proactive maintenance, significantly reducing costly downtime and enhancing the reliability of critical unmanned facilities.
Scene-LSTM is a model designed for human trajectory prediction, utilizing Long Short-Term Memory (LSTM) neural networks. This work focuses on applying artificial intelligence techniques to understand and anticipate movements in complex scenarios.
Este artigo propõe um novo framework para aprimorar a previsão de eventos El Niño, integrando dados de previsão do tempo e geográficos. Ele utiliza uma arquitetura híbrida de deep learning, combinando CNN para extração espacial e LSTM para modelagem temporal, visando identificar precursores complexos.
This article discusses practical constraints for deploying AI models, specifically LSTM, in retail environments, emphasizing budget-conscious setups for small to medium-sized companies. It highlights demand forecasting for inventory management as a key use case requiring efficient model deployment on edge devices.