Search
Skip to Search Results- 5Machine learning
- 1Aquatic species
- 1Artificial neural networks (ANNs)
- 1Automatic learning
- 1Bayesian network
- 1Construction modeling
- 3Mark A. Lewis
- 3Russell Greiner
- 2Mélodie Kunegel-Lion
- 2Pouria Ramazi
- 1Fayek, Aminah Robinson
- 1Fisher, Gary
- 3Biological Sciences, Department of
- 3Biological Sciences, Department of/Journal Articles (Biological Sciences)
- 1Civil and Environmental Engineering, Department of
- 1Civil and Environmental Engineering, Department of/Research Materials (Civil & Environmental Engineering)
- 1Mathematical and Statistical Sciences, Department of
- 1Mathematical and Statistical Sciences, Department of/Research Publications (Mathematical and Statistical Sciences)
-
2020-11-01
Seresht, Nima Gerami, Lourenzutti, Rodolfo, Fayek, Aminah Robinson
Fuzzy inference systems (FISs) are a predictive modeling technique based on fuzzy sets that utilize approximate reasoning to mimic the decision-making process of human experts. There are several expert- and data-driven methods for developing FISs, among which fuzzy clustering algorithms are the...
-
Computational investigation of the effect of microstructure on the scratch resistance of tungsten-carbide nickel composite coatings
Download2021-08-15
Parsazadeh, Mohammad, Fisher, Gary, McDonald, André, Hogan, James D.
Sliding wear was simulated for tungsten carbide-nickel (WC-Ni) composites with different WC particle sizes and volume fractions under various normal forces. Johnson-Cook and Johnson-Holmquist models were employed to simulate the mechanical behaviour of the Ni and WC phases, respectively. Using...
-
Evaluation of machine learning methods for predicting eradication of aquatic invasive species
Download2018-03-27
Yanyu Xiao, Russell Greiner, Mark A. Lewis
In the work, we evaluate the performance of machine learning approaches for predicting successful eradication of aquatic invasive species (AIS) and assess the extent to which eradication of an invasive species depends on the certain specified ecological features of the target ecosystem...
-
2021-01-05
Pouria Ramazi, Mélodie Kunegel-Lion, Russell Greiner, Mark A. Lewis
Although ecological models used to make predictions from underlying covariates have a record of success, they also suffer from limitations. They are typically unable to make predictions when the value of one or more covariates is missing during the testing. Missing values can be estimated but...
-
2021-10-01
Pouria Ramazi, Mélodie Kunegel-Lion, Russell Greiner, Mark A. Lewis
Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction...