Alireza Arabameri
Management, Monitoring, Policy and Law · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Management, Monitoring, Policy and Law — 27 citations into it
- Global and Planetary Change — 24 citations into it
- Mechanical Engineering — 9 citations into it
- Artificial Intelligence — 6 citations into it
Whom Alireza Arabameri trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Alireza Arabameri's perspective
Ordered by how strongly the researchers Alireza Arabameri trusts endorse each paper. Same corpus, a different order for every researcher.
- Greedy function approximation: A gradient boosting machine.
- Arcing classifier (with discussion and a rejoinder by the author)
- A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS (Computational Geosciences)
- Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery
- Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (Swara)
- Assessment of regions priority for implementation of solar projects in Iran: New application of a hybrid multi-criteria decision making approach
- Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest (2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS))
- High-speed three-dimensional shape measurement for dynamic scenes using bi-frequency tripolar pulse-width-modulation fringe projection
- An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. (Science of the Total Environment)
- Financial time series forecasting using independent component analysis and support vector regression
Their papers
- Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms (2021) — 155 citations
- An ensemble model for landslide susceptibility mapping in a forested area (2020) — 74 citations
- GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms (2019) — 127 citations