Amir Mosavi

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 12 papers; trusts 27 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #28,343 of 35,345 by trust here, #22,463 by citations
• Global and Planetary Change — 47 citations into it; #6,879 of 10,225 by trust here, #6,088 by citations
• Environmental Engineering — 46 citations into it; #3,916 of 5,479 by trust here, #4,524 by citations
• Electrical and Electronic Engineering — 40 citations into it
• Water Science and Technology — 40 citations into it
• Management Science and Operations Research — 36 citations into it
• Civil and Structural Engineering — 17 citations into it
• Economics and Econometrics — 13 citations into it

Whom Amir Mosavi trusts overall

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

1. Ozgur Kisi
2. Mohammad Valipour
3. Thomas G. Dietterich
4. F. Mekanik
5. Kwok-Wing Chau
6. K. Haddad
7. Andy Liaw
8. Mei Zhao
9. G. Huang
10. Shahaboddin Shamshirband

Who trusts Amir Mosavi overall

1. Zhongrun Xiang
2. June Yan

Papers from Amir Mosavi's perspective

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

1. The Nature of Statistical Learning Theory
2. Support-Vector Networks
3. Random Forests
4. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds.
5. A decision-theoretic generalization of on-line learning and an application to boosting
6. ImageNet classification with deep convolutional neural networks
7. Optimization by Simulated Annealing
8. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand…
9. Generalized Linear Models
10. Statistical Learning Theory

Their papers

• Deep Learning for Stock Market Prediction (2020) — 370 citations
• Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression (2021) — 90 citations
• Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries (2020) — 145 citations
• Comparative study of multilayer perceptron-stochastic gradient descent and gradient boosted trees for predicting daily suspended sediment load: The case study of the Mississippi River, U.S. (2020) — 55 citations
• Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020) — 54 citations
• Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020) — 345 citations
• COVID-19 Outbreak Prediction with Machine Learning (2020) — 438 citations
• State of the Art of Machine Learning Models in Energy Systems, a Systematic Review (2019) — 516 citations
• Sustainable Business Models: A Review (2019) — 539 citations
• An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. (2019) — 688 citations
• Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method. (2019) — 304 citations
• Flood Prediction Using Machine Learning Models: Literature Review (2018) — 1,396 citations