Utilizing a Long Short-Term Memory Algorithm Modified by Dwarf Mongoose Optimization to Predict Thermal Expansion of Cu-Al<sub>2</sub>O<sub>3</sub> Nanocomposites

Utilizing a Long Short-Term Memory Algorithm Modified by Dwarf Mongoose Optimization to Predict Thermal Expansion of Cu-Al<sub>2</sub>O<sub>3</sub> Nanocomposites

This paper presents a machine learning model to predict the effect of Al<sub>2</sub>O<sub>3</sub> nanoparticle content on the coefficient of thermal expansion in Cu-Al<sub>2</sub>O<sub>3</sub> nanocomposites prepared using an in situ chemical technique...

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Bibliographic Details
Main Authors: Ayman M. Sadoun, Ismail R. Najjar, Ghazi S. Alsoruji, Ahmed Wagih, Mohamed Abd Elaziz
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Mathematics
Subjects:
metal matrix nanocomposites
thermal properties
artificial neural network
dwarf mongoose optimization (DMO)
long short-term memory (LSTM)
Online Access:https://www.mdpi.com/2227-7390/10/7/1050

Internet

https://www.mdpi.com/2227-7390/10/7/1050

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