Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm

Author:

Alade Ibrahim Olanrewaju,Abd Rahman Mohd Amiruddin,Saleh Tawfik A.

Funder

Ministry of Higher Education Malaysia

Publisher

Elsevier BV

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Reference54 articles.

1. Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach;Adewumi;Appl. Soft Comput. J.,2016

2. Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine;Ahmadi;Int. J. Ambient Energy,2016

3. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach;Ahmadi;J. Therm. Anal. Calorim.,2018

4. Modeling and prediction of the specific heat capacity of Al2O3/water nanofluids using hybrid genetic algorithm/support vector regression model;Alade;Nano-Struct. Nano-Objects,2019

5. Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm - support vector machine approach;Alade;Comput. Methods Programs Biomed.,2018

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