M
Mohsen Hajihassani
Researcher at Urmia University
Publications - 59
Citations - 2993
Mohsen Hajihassani is an academic researcher from Urmia University. The author has contributed to research in topics: Particle swarm optimization & Computer science. The author has an hindex of 21, co-authored 51 publications receiving 2094 citations. Previous affiliations of Mohsen Hajihassani include Universiti Teknologi Malaysia & Duy Tan University.
Papers
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Journal ArticleDOI
Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization
D. Jahed Armaghani,Mohsen Hajihassani,E. Tonnizam Mohamad,Aminaton Marto,Seyed Ahmad Noorani +4 more
TL;DR: A novel approach of incorporating PSO algorithm with ANN has been proposed to eliminate the limitation of the BP-ANN and the results indicate that the proposed method is able to predict flyrock distance and PPV induced by blasting with a high degree of accuracy.
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Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks
TL;DR: Comparison between the coefficients of determination, R2, obtained through conventional ANN and PSO-based ANN techniques reveal the superiority of the PSO -based ANN model in predicting UCS.
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Prediction of seismic slope stability through combination of particle swarm optimization and neural network
TL;DR: It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN and R2 values of testing datasets equal to 0.915 and 0.986 suggest the superiority of thePSO– ANN technique.
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Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
TL;DR: In this article, a hybrid artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) was proposed to predict peak particle velocity (PPV) resulting from quarry blasting.
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Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
TL;DR: A new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting is presented and it is suggested that the PSO-based ANN model outperforms the other predictive models.