long short-term memory (LSTM)

A hybrid model for predicting air quality combining Holt–Winters and Deep Learning Approaches: A novel method to identify ozone concentration peaks

Ozone (O$_3$) from the troposphere is one of the substances that has a strong effect on air pollution in the city of Tanger.  Prediction of this pollutant can have positive improvements in air quality.  This paper presents a new approach combining deep-learning algorithms and the Holt–Winters method in order to detect pollutant peaks and obtain a more accurate forecasting model.  Given that LSTM is an extremely powerful algorithm, we hybridized with the Holt–Winters method to enhance the model.  Making use of multiple accuracy metrics, the models' efficiency is investig

A generic model of the information and decisional chain using Machine Learning based assistance in a manufacturing context

Nowadays, manufacturers must deal with huge international competition and continually improve their performances.  In this context, several essential approaches namely CBM (Condition-based maintenance), PHM (Prognostics and Health Management), and PLM (Product Lifecycle Management) are used for manufacturing systems to maintain and increase their availability, reliability and performance.  This implies that operational usage data of the manufacturing equipment must then be made available to all stakeholders concerned through efficient informational chains.  However confronted with a large a