Jian Zhou

Artificial Intelligence · Engineering computations · 2016–2021

A researcher in the Science corpus: 11 papers; trusts 11 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Civil and Structural Engineering — 31 citations into it
• Statistics and Probability — 28 citations into it
• Computational Theory and Mathematics — 18 citations into it
• Mechanics of Materials — 17 citations into it; #3,348 of 6,291 by trust here, #1,774 by citations
• Safety, Risk, Reliability and Quality — 17 citations into it
• Ocean Engineering — 15 citations into it; #1,520 of 3,193 by trust here, #402 by citations
• Statistical Learning Theory — 10 citations into it

Whom Jian Zhou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jerome H. Friedman
2. Wengang Zhang
3. Seyedali Mirjalili
4. Danial Jahed Armaghani
5. Tianqi Chen
6. Leo Breiman
7. Xibing Li
8. Max Kuhn
9. Trevor J. Hastie
10. Vladimir Naumovich Vapni

Who trusts Jian Zhou overall

1. Yingui Qiu
2. Wengang Zhang
3. Jin Duan
4. Chuanqi Li
5. Manoj Khandelwal
6. Danial Jahed Armaghani
7. Panagiotis G. Asteris
8. Chongzhi Wu
9. Haitao Yang
10. Hoang Nguyen

Papers from Jian Zhou's perspective

Ordered by how strongly the researchers Jian Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Classification and Regression Trees
2. The Nature of Statistical Learning Theory
3. Probabilistic reasoning in intelligent systems - networks of plausible inference
4. A new optimizer using particle swarm theory
5. SPLIT SELECTION METHODS FOR CLASSIFICATION TREES
6. An Introduction to Genetic Algorithms.
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition (IEEE Congress on Evolutionary Computation)
9. Regression Shrinkage and Selection via the Lasso
10. ImageNet classification with deep convolutional neural networks

Their papers

• A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning (2019) — 298 citations
• Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors (2021) — 104 citations
• Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate (2021) — 297 citations
• Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration (2021) — 447 citations
• Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites (2020) — 91 citations
• Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques (2020) — 245 citations
• Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization (2020) — 240 citations
• Stability analysis of underground mine hard rock pillars via combination of finite difference methods, neural networks, and Monte Carlo simulation techniques (2020) — 93 citations
• Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories (2019) — 314 citations
• Evaluation method of rockburst: State-of-the-art literature review (2018) — 436 citations
• Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods (2016) — 370 citations