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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Journal ArticleDOI
Machine learning modelling for predicting soil liquefaction susceptibility
Pijush Samui,T. G. Sitharam +1 more
TL;DR: This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake and highlights the capability of the SVM over the ANN models.
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Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models
TL;DR: It is advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.
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Assessment of pile drivability using random forest regression and multivariate adaptive regression splines
TL;DR: A practical approach to assess pile drivability in relation to the prediction of Maximum compressive stresses and Blow per foot using a series of machine learning algorithms is presented.
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Utilization of a least square support vector machine (LSSVM) for slope stability analysis
Pijush Samui,D.P. Kothari +1 more
TL;DR: This study shows that the developed LSSVM is a robust model for slope stability analysis.
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Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
Viet-Ha Nhu,Nhat-Duc Hoang,Hieu Nguyen,Phuong Thao Thi Ngo,Tinh Thanh Bui,Pham Viet Hoa,Pijush Samui,Dieu Tien Bui +7 more
TL;DR: It is concluded that the Keras’s deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas and is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree.