Search alternatives:
code optimization » codon optimization (Expand Search), model optimization (Expand Search), dose optimization (Expand Search)
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binary data » dietary data (Expand Search)
data global » daily global (Expand Search)
data code » data model (Expand Search), data came (Expand Search)
code optimization » codon optimization (Expand Search), model optimization (Expand Search), dose optimization (Expand Search)
primary data » primary care (Expand Search)
binary data » dietary data (Expand Search)
data global » daily global (Expand Search)
data code » data model (Expand Search), data came (Expand Search)
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41
The proportion integral derivative controller.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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42
Random parameter factor.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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43
Eight commonly used benchmark functions.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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44
Hyperbolic tangent row domain.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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45
Parameter settings.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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46
Nonlinear fast convergence factor.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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47
CEC2019 benchmark functions.
Published 2023“…The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. …”
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48
Performance metrics for BrC.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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49
Proposed CVAE model.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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50
Proposed methodology.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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51
Loss vs. Epoch.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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52
Sample images from the BreakHis dataset.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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53
Accuracy vs. Epoch.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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54
Segmentation results of the proposed model.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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55
S1 Dataset -
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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56
CSCO’s flowchart.
Published 2024“…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. …”
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