بدائل البحث:
driven optimization » design optimization (توسيع البحث), guided optimization (توسيع البحث), dose optimization (توسيع البحث)
scale optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), phase optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
driven optimization » design optimization (توسيع البحث), guided optimization (توسيع البحث), dose optimization (توسيع البحث)
scale optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), phase optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
-
1
-
2
-
3
-
4
-
5
-
6
-
7
-
8
-
9
-
10
-
11
-
12
Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
منشور في 2019"…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …"
-
13
-
14
-
15
-
16
Sample image for illustration.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
-
17
Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
منشور في 2024"…The model divides the soil profile into topsoil (0-20 cm) and subsoil (20–100 cm) layers to match the SOC maps of the corresponding two layers generated by data-driven models. Each of these layers contains a young carbon pool (CY) with a higher decomposition rate and an old carbon pool (CO) with a lower decomposition rate. …"
-
18
Quadratic polynomial in 2D image plane.
منشور في 2024"…The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. …"
-
19
Steps in the extraction of 14 coordinates from the CT slices for the curved MPR.
منشور في 2025"…The image is then cleaned in c) using morphological filtering with an <i>opening</i> operation to remove small-scale noise. …"
-
20