بدائل البحث:
method algorithm » network algorithm (توسيع البحث), means algorithm (توسيع البحث), mean algorithm (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
elements method » element method (توسيع البحث)
complement ipca » complement 5a (توسيع البحث), complement c3 (توسيع البحث), complement c5 (توسيع البحث)
ipca algorithm » wgcna algorithm (توسيع البحث), cscap algorithm (توسيع البحث), ii algorithm (توسيع البحث)
level coding » level according (توسيع البحث), level modeling (توسيع البحث), level using (توسيع البحث)
method algorithm » network algorithm (توسيع البحث), means algorithm (توسيع البحث), mean algorithm (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
elements method » element method (توسيع البحث)
complement ipca » complement 5a (توسيع البحث), complement c3 (توسيع البحث), complement c5 (توسيع البحث)
ipca algorithm » wgcna algorithm (توسيع البحث), cscap algorithm (توسيع البحث), ii algorithm (توسيع البحث)
level coding » level according (توسيع البحث), level modeling (توسيع البحث), level using (توسيع البحث)
-
481
Oscillatory Field Genesis: The Emergent Architecture of Spacetime, Matter, and Memory
منشور في 2025الموضوعات: -
482
Quantitative results on WEDU dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
483
Counting results on DRPD dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
484
Quantitative results on RFRB dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
485
Main module structure.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
486
Counting results on MTDC-UAV dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
487
Quantitative results on DRPD dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
488
Architecture of MAR-YOLOv9.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
489
Quantitative results on MTDC-UAV dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
490
Counting results on WEDU dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
491
Example images from four plant datasets.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
492
Counting results on RFRB dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
493
Detection visualization results on WEDU dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
-
494
-
495
Structure of optimized model parameters in the high-dimensional cases.
منشور في 2025"…The number and size of the clusters were determined with help of the -means clustering method. Both were set to zero if the absolute mean value of the off-diagonal elements in the correlation matrix (cf. …"
-
496
Design of stiffened panels for stress and buckling via topology optimization: data
منشور في 2024"…To solve the optimization problem, a semi-analytical sensitivity analysis is performed, and the optimization algorithm is outlined. Numerical investigations demonstrate and validate the proposed method.…"
-
497
Data Sheet 1_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl...
منشور في 2025"…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R<sup>2</sup> ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …"
-
498
Data Sheet 2_Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (Glycyrrhiza uralensis Fisch.) based on unmanned aerial vehicl...
منشور في 2025"…PIs collectively achieved high-precision predictions (mean 0.42 ≤ R<sup>2</sup> ≤ 0.94), with the prediction of PH using green leaf index (GLI) in BP algorithm attaining peak accuracy (R² = 0.94). VIs and PIs exhibited comparable predictive capacity for yield, with multi-indicators integrated modeling significantly enhancing performance: VIs achieved R² = 0.87 under RF algorithms, whereas PIs reached R² = 0.81 using BP algorithms. …"
-
499
Methodological overview.
منشور في 2025"…<p>(A) The source reconstruction of TMS-evoked potential of each subject was performed using dSPM method based on MNE software library. The time series of cortical activity were extracted through Schaefer 200 parcellation atlas. …"
-
500
Mean squared Error on all unseen data.
منشور في 2025"…The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. Next, we examine the statistical effect of correlated prediction error and propose a method for Generalized Least Squares (GLS) on graphs. …"