Seyed Amin Bagherzadeh
Biomedical Engineering · 2018–2019
A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Biomedical Engineering — 102 citations into it
- Mechanical Engineering — 75 citations into it
- Computational Mechanics — 28 citations into it
Whom Seyed Amin Bagherzadeh trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arash Karimipour
- David John Cameron MacKay
- M. Hemmat Esfe
- Mohammad Reza Safaei
- Omid Ali Akbari
- Masoud Afrand
- Davood Toghraie
- Mohsen Sheikholeslami
- Fan Zhang
- Kurt Hornik
Papers from Seyed Amin Bagherzadeh's perspective
Ordered by how strongly the researchers Seyed Amin Bagherzadeh trusts endorse each paper. Same corpus, a different order for every researcher.
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
- Support Vector Regression (Wiley StatsRef: Statistics Reference Online)
- Evaluation of thermal conductivity of COOH-functionalized MWCNTs/water via temperature and solid volume fraction by using experimental data and ANN methods (Journal of Thermal Analysis and Calorimetry)
- A Numerical Study on the Forced Convection of Laminar Nanofluid in a Microchannel with Both Slip and No-Slip Conditions
- Lattice Boltzmann simulation of natural convection heat transfer of Al2O3/water nanofluids in a square enclosure
- Laminar mixed convection in shallow inclined driven cavities with hot moving lid on top and cooled from bottom
- Heat transfer enhancement by using nanofluids in forced convection flows
- A micro-convection model for thermal conductivity of nanofluids
- Heat transfer behaviours of nanofluids in a uniformly heated tube
- Application of lattice Boltzmann method to simulate microchannel flows
Their papers
- Synthesized CuFe2O4/SiO2 nanocomposites added to water/EG: Evaluation of the thermophysical properties beside sensitivity analysis & EANN (2018) — 141 citations
- A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data (2019) — 149 citations
- A novel sensitivity analysis model of EANN for F-MWCNTs–Fe3O4/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs (2019) — 137 citations