بدائل البحث:
cell optimization » field optimization (توسيع البحث), wolf optimization (توسيع البحث), lead optimization (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
genes based » gene based (توسيع البحث), lens based (توسيع البحث)
based swarm » based sars (توسيع البحث), based smart (توسيع البحث), based arm (توسيع البحث)
cell optimization » field optimization (توسيع البحث), wolf optimization (توسيع البحث), lead optimization (توسيع البحث)
binary based » library based (توسيع البحث), linac based (توسيع البحث), binary mask (توسيع البحث)
genes based » gene based (توسيع البحث), lens based (توسيع البحث)
based swarm » based sars (توسيع البحث), based smart (توسيع البحث), based arm (توسيع البحث)
-
1
-
2
-
3
A* Path-Finding Algorithm to Determine Cell Connections
منشور في 2025"…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. …"
-
4
Table1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.docx
منشور في 2024"…The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …"
-
5
DataSheet1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.ZIP
منشور في 2024"…The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …"
-
6
-
7
-
8
-
9
-
10
GSE96058 information.
منشور في 2024"…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …"
-
11
The performance of classifiers.
منشور في 2024"…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …"
-
12
-
13
-
14
-
15
-
16
-
17
-
18
Image_2_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF
منشور في 2022"…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …"
-
19
Image_1_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF
منشور في 2022"…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …"
-
20
Image_3_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF
منشور في 2022"…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …"