بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm brain » algorithm ai (توسيع البحث), algorithm against (توسيع البحث), algorithm within (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
brain function » barrier function (توسيع البحث), protein function (توسيع البحث)
both function » body function (توسيع البحث), growth function (توسيع البحث), beach function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث)
python function » protein function (توسيع البحث)
algorithm brain » algorithm ai (توسيع البحث), algorithm against (توسيع البحث), algorithm within (توسيع البحث)
algorithm both » algorithm blood (توسيع البحث), algorithm b (توسيع البحث), algorithm etc (توسيع البحث)
brain function » barrier function (توسيع البحث), protein function (توسيع البحث)
both function » body function (توسيع البحث), growth function (توسيع البحث), beach function (توسيع البحث)
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41
Ablation study on the RSOD dataset.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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42
Structure and working principle of LI-YOLOv8.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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43
C2f-E improvement process.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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44
Structure of Detect and GP-Detect.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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45
YOLOv8 structure and working principle.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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46
Improvement of CBS to CBR process.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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47
EMA attention mechanism working principle.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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48
Ablation study on the NWPU VHR-10 dataset.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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49
GSConv working principle.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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50
PR comparison on NWPU VHR-10 dataset.
منشور في 2025"…Using YOLOv8n as the baseline algorithm, the activation function SiLU in the CBS at the backbone network’s SPPF is replaced with ReLU, which reduces interdependencies among parameters. …"
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51
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52
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53
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54
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55
The pseudocode for the NAFPSO algorithm.
منشور في 2025"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"
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56
PSO algorithm flowchart.
منشور في 2025"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"
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57
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58
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59
Comparison of different algorithms.
منشور في 2025"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"
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60
Logistic kernel: Power and Type I error for cluster evaluation metric simulations.
منشور في 2024الموضوعات: