Search alternatives:
driver optimization » driven optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
whole optimization » whale optimization (Expand Search), wolf optimization (Expand Search), dose optimization (Expand Search)
library based » laboratory based (Expand Search)
based whole » used whole (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a driver » _ drivers (Expand Search), _ drive (Expand Search), _ driven (Expand Search)
driver optimization » driven optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
whole optimization » whale optimization (Expand Search), wolf optimization (Expand Search), dose optimization (Expand Search)
library based » laboratory based (Expand Search)
based whole » used whole (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
a driver » _ drivers (Expand Search), _ drive (Expand Search), _ driven (Expand Search)
-
1
Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP
Published 2022“…In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …”
-
2
Table_3_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
3
Image_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
4
Image_2_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
5
DataSheet_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.docx
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
6
Image_3_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.jpeg
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
7
Table_4_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
8
Table_2_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”
-
9
Table_1_G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction.xlsx
Published 2023“…Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. …”