activity
20192021
collaborators

8 papers

q-bio.GN2021

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…

q-bio.QM2021

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…

cs.LG2020

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…

q-bio.QM2020

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…

stat.ML2020

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…

stat.AP2020

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…