A
Aref Mardani
Researcher at Urmia University
Publications - 56
Citations - 1191
Aref Mardani is an academic researcher from Urmia University. The author has contributed to research in topics: Artificial neural network & Rolling resistance. The author has an hindex of 18, co-authored 54 publications receiving 922 citations.
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Investigating the effect of velocity, inflation pressure, and vertical load on rolling resistance of a radial ply tire
Hamid Taghavifar,Aref Mardani +1 more
TL;DR: In this article, a single wheel tester at Department of Agricultural Machinery of Urmia University was utilized to investigate the effect of velocity, tire inflation pressure, and vertical load on rolling resistance of wheel.
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A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility
TL;DR: It is elucidated that hybrid ICA–ANN further succeeded to denote lower modeling error amongst which, cascade-forward network optimized by ICA managed to yield the highest quality solutions.
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Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network
Hamid Taghavifar,Aref Mardani +1 more
TL;DR: In this paper, the potential of a supervised Artificial Neural Network (ANN) approach was assessed to prognosticate the energy consumption and environmental indices of apple production in the studying location.
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Appraisal of artificial neural networks to the emission analysis and prediction of CO2, soot, and NOx of n-heptane fueled engine
Hadi Taghavifar,Hamid Taghavifar,Aref Mardani,Arash Mohebbi,Shahram Khalilarya,Samad Jafarmadar +5 more
TL;DR: In this article, a code was developed to simulate the combustion process using computational fluid dynamics (CFD) approach employing n-heptane fuel under the effect of crank angle, temperature, pressure, liquid mass evaporated, equivalence ratio, and O 2 concentration at two engine speeds of 2000 and 3000rpm.
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Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices
Hamid Taghavifar,Aref Mardani +1 more
TL;DR: In this article, a feed-forward ANN with standard BP (back propagation) algorithm was used to construct a supervised representation to predict the energy efficiency indices of driven wheels (i.e. traction coefficient and tractive power efficiency) as affected by wheel load, slippage and forward velocity at three different levels with three replicates to form a total of 162 data points.