Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid
In this paper, we developed dissimilar artificial neural networks (ANNs) by suitable
architectures and training algorithms via sensitivity analysis to predict the thermal …
architectures and training algorithms via sensitivity analysis to predict the thermal …
Significance of EMHD graphene oxide (GO) water ethylene glycol nanofluid flow in a Darcy–Forchheimer medium by machine learning algorithm
The low heat efficiency of base fluids is a key problem among investigators. To address this
issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to …
issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to …
Identifying applications of machine learning and data analytics based approaches for optimization of upstream petroleum operations
Over the past few years, machine learning and data analytics have gained tremendous
attention as emerging trends in the oil and gas industry. The usage of modern tools and high …
attention as emerging trends in the oil and gas industry. The usage of modern tools and high …
Modeling of CO2 capture ability of [Bmim][BF4] ionic liquid using connectionist smart paradigms
The burning of fossil fuels produces large amounts of exhaust gases containing carbon
dioxide (CO 2). The emission of CO 2 into the atmosphere is widely known as the leading …
dioxide (CO 2). The emission of CO 2 into the atmosphere is widely known as the leading …
Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks
In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been
predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1 …
predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1 …
[HTML][HTML] Comparative study of artificial neural network versus parametric method in COVID-19 data analysis
Since the previous two years, a new coronavirus (COVID-19) has found a major global
problem. The speedy pathogen over the globe was followed by a shockingly large number …
problem. The speedy pathogen over the globe was followed by a shockingly large number …
Oil logging reservoir recognition based on TCN and SA-BiLSTM deep learning method
W Yang, K Xia, S Fan - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Abstract The use of Deep Learning methods to mine useful and critical information from
massive and complex logging datasets is of great importance for oil logging reservoir …
massive and complex logging datasets is of great importance for oil logging reservoir …
Machine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection
Mixed convection phenomenon over radiant cooled surfaces with displacement ventilation
in living environments is becoming a popular issue due to the airborne viruses and energy …
in living environments is becoming a popular issue due to the airborne viruses and energy …
Significance of bioconvective flow of MHD thixotropic nanofluid passing through a vertical surface by machine learning algorithm
Scientists have made significant contributions in the current decade due to the importance of
bioconvection in biotechnology and a variety of biological systems. In this study, a …
bioconvection in biotechnology and a variety of biological systems. In this study, a …
Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network
In this paper, at first, a new correlation was proposed to predict the relative viscosity of
MWCNTs-SiO 2/AE40 nano-lubricant using experimental data. Then, considering minimum …
MWCNTs-SiO 2/AE40 nano-lubricant using experimental data. Then, considering minimum …