Pijush Samui
Civil and Structural Engineering · 2006–2021
A researcher in the Science corpus: 8 papers; trusts 24 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Civil and Structural Engineering — 36 citations into it
- Artificial Intelligence — 30 citations into it; #16,355 of 35,345 by trust here, #19,018 by citations
- Environmental Engineering — 21 citations into it; #1,815 of 5,479 by trust here, #2,092 by citations
- Global and Planetary Change — 13 citations into it; #5,044 of 10,225 by trust here, #7,466 by citations
- Computer Vision and Pattern Recognition — 10 citations into it
- Water Science and Technology — 10 citations into it
- Electrical and Electronic Engineering — 9 citations into it; #9,997 of 16,539 by trust here, #10,438 by citations
- Statistics and Probability — 9 citations into it
Whom Pijush Samui trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David John Cameron MacKay
- MohammadSajjad Khan
- Richard G. Brereton
- Jerome H. Friedman
- James O. Berger
- Paulin Coulibaly
- Simon S. Haykin
- Ravinesh C. Deo
- C. Sivapragasam
- George Eastman House
Who trusts Pijush Samui overall
- Ravinesh C. Deo
- Ozgur Kisi
- Mohammad Ali Ghorbani
- Zhenliang Yin
- Zaher Mundher Yaseen
- Dookie Kim
- Salim Heddam
Papers from Pijush Samui's perspective
Ordered by how strongly the researchers Pijush Samui trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- The Nature of Statistical Learning Theory
- Bagging Predictors
- A training algorithm for optimal margin classifiers
- Statistical Learning Theory
- Smoothing noisy data with spline functions
- Random Forests
- Neural Networks: A Comprehensive Foundation
- Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks
- ImageNet classification with deep convolutional neural networks
Their papers
- Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016) — 137 citations
- Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs (2012) — 84 citations
- Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models (2021) — 615 citations
- Compressive strength prediction of high-performance concrete using gradient tree boosting machine (2020) — 192 citations
- A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. (2019) — 361 citations
- Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019) — 187 citations
- Forecasting Evaporative Loss by Least-Square Support-Vector Regression and Evaluation with Genetic Programming, Gaussian Process, and Minimax Probability Machine Regression: Case Study of Brisbane City (2017) — 47 citations
- Stability determination for layered soil slopes using the upper bound limit analysis (2006) — 74 citations