بدائل البحث:
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
led optimization » lead optimization (توسيع البحث), yet optimization (توسيع البحث), based optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data model » data models (توسيع البحث)
data led » data lead (توسيع البحث)
model optimization » codon optimization (توسيع البحث), global optimization (توسيع البحث), based optimization (توسيع البحث)
led optimization » lead optimization (توسيع البحث), yet optimization (توسيع البحث), based optimization (توسيع البحث)
binary data » primary data (توسيع البحث), dietary data (توسيع البحث)
data model » data models (توسيع البحث)
data led » data lead (توسيع البحث)
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41
Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports
منشور في 2020"…<p> The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical features from free-text medical notes. …"
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42
IRBMO vs. variant comparison adaptation data.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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43
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44
DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf
منشور في 2022"…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…"
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46
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47
Pseudo Code of RBMO.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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48
P-value on CEC-2017(Dim = 30).
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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49
Memory storage behavior.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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50
Elite search behavior.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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51
Description of the datasets.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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52
S and V shaped transfer functions.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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53
S- and V-Type transfer function diagrams.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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54
Collaborative hunting behavior.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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55
Friedman average rank sum test results.
منشور في 2025"…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …"
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56
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57
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58
Generalized Tensor Decomposition With Features on Multiple Modes
منشور في 2021"…Our proposal handles a broad range of data types, including continuous, count, and binary observations. …"
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60
An Example of a WPT-MEC Network.
منشور في 2025"…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …"