439 citations · 1.2k across the 8 of their papers we have counts for
8 papers
A reinforcement learning approach to resource allocation in genomic selection
Saba Moeinizade, Guiping Hu, Lizhi Wang
Genomic selection (GS) is a technique that plant breeders use to select individuals to mate and produce new generations of species. Allocation of resources is a key factor in GS. A…
Corn Yield Prediction with Ensemble CNN-DNN
Mohsen Shahhosseini, Guiping Hu, Saeed Khaki +1
We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The deve…
Improved Weighted Random Forest for Classification Problems
Mohsen Shahhosseini, Guiping Hu
Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key t…
Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt
Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis +1
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a h…
A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net
Fatemeh Amini, Guiping Hu
Feature selection, as a critical pre-processing step for machine learning, aims at determining representative predictors from a high-dimensional feature space dataset to improve th…
Forecasting Corn Yield with Machine Learning Ensembles
Mohsen Shahhosseini, Guiping Hu, Sotirios V. Archontoulis
The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent researc…
Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms
Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu +1
Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scen…
Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
Mohsen Shahhosseini, Guiping Hu, Hieu Pham
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensemblin…