Search alternatives:
process optimization » model optimization (Expand Search)
based optimization » whale optimization (Expand Search)
library based » laboratory based (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
data based » data used (Expand Search)
process optimization » model optimization (Expand Search)
based optimization » whale optimization (Expand Search)
library based » laboratory based (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
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61
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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62
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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63
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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64
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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65
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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66
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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67
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. …”
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68
<i>hi</i>PRS algorithm process flow.
Published 2023“…<p><b>(A)</b> Input data is a list of genotype-level SNPs. <b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”
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69
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70
Comparison in terms of the sensitivity.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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71
Parameter sensitivity of BIMGO.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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72
Details of the medical datasets.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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73
The flowchart of IMGO.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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74
Comparison in terms of the selected features.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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75
Iterative chart of control factor.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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76
Details of 23 basic benchmark functions.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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77
Related researches.
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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78
S1 Dataset -
Published 2024“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”
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79
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80
A simple HFS instance.
Published 2025“…In recent years, due to the advantages of nonlinear access and fully parallel processing, the probe machine has shown powerful computing capabilities and promising applications in solving various combinatorial optimization problems. …”