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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Determination of compressive strength using relevance vector machine and emotional neural network
TL;DR: In this paper, the capability of relevance vector machine (RVM) and emotional neural network (ENN) for determination of compressive strength using the efficiency factor, percentage silica fume replacement and ultrasonic pulse velocity (UPV) as an input parameter.
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Spatial variability of rock depth using adaptive neuro-fuzzy inference system (ANFIS) and multivariate adaptive regression spline (MARS)
TL;DR: In this paper, adaptive neuro-fuzzy inference system (ANFIS) and multi adaptive regression spline (MARS) were adopted for prediction of spatial variability of reduced level of rock depth (d) in Bangalore.
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Determination of liquefaction susceptibility of soil: a least square support vector machine approach
Pijush Samui,J. Karthikeyan +1 more
TL;DR: In this article, the authors employed Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility of soil using dataset from the 1999 Chi-Chi, Taiwan earthquake.
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Newly explored machine learning model for river flow time series forecasting at Mary River, Australia.
TL;DR: The present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.
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Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study
TL;DR: The proposed Deep Learning model to predict groundwater depths is an intelligent tool for predicting groundwater depths and can save resources and labor conventionally employed to estimate various features of complex groundwater systems.