Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization

Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization

In this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites. Th...

Full description

Bibliographic Details
Main Authors: Ismail R. Najjar, Ayman M. Sadoun, Adel Fathy, Ahmed W. Abdallah, Mohamed Abd Elaziz, Marwa Elmahdy
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Lubricants
Subjects:
long short-term model
golden jackal optimization
Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites
tribological properties
Online Access:https://www.mdpi.com/2075-4442/10/11/277

Internet

https://www.mdpi.com/2075-4442/10/11/277

Similar Items