A. Hemmati-Sarapardeh

Computational Theory and Mathematics · 2013–2022

A researcher in the Science corpus: 15 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 12 citations into it
• Food Science — 5 citations into it
• Health, Toxicology and Mutagenesis — 5 citations into it

Whom A. Hemmati-Sarapardeh trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Johan A. K. Suykens
2. Paola Gramatica
3. L. S. Sundar
4. Peter J. Rousseeuw
5. Martin Fodslette Møller
6. Martin T. Hagan
7. M. Hemmat Esfe
8. David John Cameron MacKay

Papers from A. Hemmati-Sarapardeh's perspective

Ordered by how strongly the researchers A. Hemmati-Sarapardeh trusts endorse each paper. Same corpus, a different order for every researcher.

1. Prediction of sour gas compressibility factor using an intelligent approach
2. Thermal conductivity and particle agglomeration in alumina nanofluids: experiment and theory. (Physical review. E, Statistical, nonlinear, and soft matter physics)
3. ANOMALOUSLY INCREASED EFFECTIVE THERMAL CONDUCTIVITIES OF ETHYLENE GLYCOL-BASED NANOFLUIDS CONTAINING COPPER NANOPARTICLES
4. A Benchmark Study on the Thermal Conductivity of Nanofluids
5. The Nature of Statistical Learning Theory
6. Genetic Algorithms in Search Optimization and Machine Learning
7. Mechanisms of heat flow in suspensions of nano-sized particles (nanofluids)
8. Natural convection of nano-fluids
9. Heat transfer enhancement by using nanofluids in forced convection flows
10. Support-Vector Networks

Their papers

• On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment (2018) — 239 citations
• Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade-forward back-propagation network (2022) — 57 citations
• Predicting viscosity of CO2–N2 gaseous mixtures using advanced intelligent schemes (2022) — 53 citations
• Toward smart schemes for modeling CO2 solubility in crude oil: Application to carbon dioxide enhanced oil recovery (2021) — 51 citations
• Modeling minimum miscibility pressure of pure/impure CO2-crude oil systems using adaptive boosting support vector regression: Application to gas injection processes (2020) — 53 citations
• Comparison of LSSVM model results with artificial neural network model for determination of the solubility of SO2 in ionic liquids (2020) — 46 citations
• Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: Application to thermal enhanced oil recovery processes (2019) — 59 citations
• Modeling oil-brine interfacial tension at high pressure and high salinity conditions (2019) — 65 citations
• Modeling minimum miscibility pressure during pure and impure CO 2 flooding using hybrid of radial basis function neural network and evolutionary techniques (2018) — 97 citations
• Modeling interfacial tension in N2/n-alkane systems using corresponding state theory: Application to gas injection processes (2018) — 50 citations
• Toward mechanistic understanding of asphaltene aggregation behavior in toluene: The roles of asphaltene structure, aging time, temperature, and ultrasonic radiation (2018) — 138 citations
• Application of adaptive neuro fuzzy interface system optimized with evolutionary algorithms for modeling CO2-crude oil minimum miscibility pressure (2017) — 93 citations
• A soft computing approach for the determination of crude oil viscosity: Light and intermediate crude oil systems (2016) — 64 citations
• Application of Wilcoxon generalized radial basis function network for prediction of natural gas compressibility factor (2015) — 61 citations
• Prediction of sour gas compressibility factor using an intelligent approach (2013) — 96 citations