P
Pijush Samui
Researcher at National Institute of Technology, Patna
Publications - 297
Citations - 5906
Pijush Samui is an academic researcher from National Institute of Technology, Patna. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 31, co-authored 236 publications receiving 3230 citations. Previous affiliations of Pijush Samui include Kunsan National University & University of Massachusetts Lowell.
Papers
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Uplift Capacity of Suction Caisson in Clay Using Artificial Intelligence Techniques
TL;DR: In this paper, Artificial Neural Networks (ANN), Genetic Programming (GP), Support Vector Machine (SVM), and Relevance Vector Machines (RVM) have been used to predict the uplift capacity of a suction caisson in clay.
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Predicting the thermal conductivity of soils using integrated approach of ANN and PSO with adaptive and time-varying acceleration coefficients
TL;DR: In this article , a hybrid adaptive neuro swarm intelligence (HANSI) technique was proposed for predicting the thermal conductivity of unsaturated soils, which integrated artificial neural networks (ANNs) and particle swarm optimisation (PSO) with adaptive and time-varying acceleration coefficients.
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A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Navid Kardani,Abidhan Bardhan,Bishwajit Roy,Pijush Samui,Majidreza Nazem,Danial Jahed Armaghani,Annan Zhou +6 more
TL;DR: In this paper, a hybrid model constructed by combination of the improved version of the Harris Hawks optimisation (HHO), and extreme learning machine (ELM) is proposed to predict the permeability of tight carbonates using limited number of input variables.
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Reliability Analysis of Pile Foundation Using ELM and MARS
Manish Kumar,Pijush Samui +1 more
TL;DR: The principal objective of the study is to examine the applicability of Extreme Learning Machine and Multivariate Adaptive Regression Spline models for predicting the bearing capacity of a pile embedded in cohesionless soil and comparing their respective performances.
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Predicting stable alluvial channel profiles using emotional artificial neural networks
TL;DR: The results indicate that EANN has the lowest Width of the Confidence Bounds (WCB) and the lowest Mean Error of Predictions (MEP) of ± 0.00004 and -0.00041 respectively compared to FFNN and GEP and also previous models.