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.
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A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.
Dieu Tien Bui,Nhat-Duc Hoang,Francisco Martínez-Álvarez,Phuong Thao Thi Ngo,Pham Viet Hoa,Tien Dat Pham,Pijush Samui,Romulus Costache +7 more
TL;DR: It could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.
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A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping
Dieu Tien Bui,Phuong Thao Thi Ngo,Tien Dat Pham,Abolfazl Jaafari,Nguyen Quang Minh,Pham Viet Hoa,Pijush Samui +6 more
TL;DR: A new soft computing approach that is an integration of an Extreme Learning Machine and a Particle Swarm Optimization, named as PSO-ELM, for the spatial prediction of flash flood susceptibility at high frequency tropical typhoon areas is proposed and validated.
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Support vector machine applied to settlement of shallow foundations on cohesionless soils
TL;DR: In this article, a support vector machine (SVM) was used to predict the settlement of shallow foundations on cohesionless soil, and a thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement.
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Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models
TL;DR: The newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.
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Slope stability analysis: a support vector machine approach
TL;DR: Support Vector Machine model, firmly based on the theory of statistical learning, is used in slope stability problem and gives better result than previously published result of ANN model for factor of safety prediction and stability status.