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Showing 1 - 20 results of 75 for search '(( binary _ structure determination algorithm ) OR ( binary wave bayesian optimization algorithm ))', query time: 0.58s Refine Results
  1. 1
    Texas Rio Grande Valley Red–crowned Parrot predicted general use habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence) MaxEnt models.

    Texas Rio Grande Valley Red–crowned Parrot predicted general use habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence... by Elise Varaela Voltura (17592996)

    Published 2023
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  2. 2
    Texas Rio Grande Valley Red–crowned Parrot predicted roost site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence) MaxEnt models.

    Texas Rio Grande Valley Red–crowned Parrot predicted roost site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

    Published 2023
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  3. 3
    Texas Rio Grande Valley Red–crowned Parrot predicted nest site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence) MaxEnt models.

    Texas Rio Grande Valley Red–crowned Parrot predicted nest site habitat distribution as determined via a consensus-based feature ensemble of 12 calibrated (binary presence/absence)... by Elise Varaela Voltura (17592996)

    Published 2023
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  4. 4
    Structural diagram of model.

    Structural diagram of model. by Ji Seung Ryu (16187857)

    Published 2023
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  5. 5
    The overlap of Texas Rio Grande Valley Red–crowned Parrot predicted general use, roost site, and nest site habitat distributions as determined using consensus-based feature ensembles of separate sets of 12 calibrated (binary presence/absence) MaxEnt models.

    The overlap of Texas Rio Grande Valley Red–crowned Parrot predicted general use, roost site, and nest site habitat distributions as determined using consensus-based feature ensembl... by Elise Varaela Voltura (17592996)

    Published 2023
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  6. 6
    Image4_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png

    Image4_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

    Published 2023
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  7. 7
    Table_1_On the Origin of Biomolecular Networks.pdf

    Table_1_On the Origin of Biomolecular Networks.pdf by Heeralal Janwa (6574409)

    Published 2019
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  8. 8
    Data_Sheet_1_On the Origin of Biomolecular Networks.PDF

    Data_Sheet_1_On the Origin of Biomolecular Networks.PDF by Heeralal Janwa (6574409)

    Published 2019
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  9. 9
    Image1_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png

    Image1_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

    Published 2023
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  10. 10
    Table_2_On the Origin of Biomolecular Networks.pdf

    Table_2_On the Origin of Biomolecular Networks.pdf by Heeralal Janwa (6574409)

    Published 2019
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  11. 11
    Image2_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png

    Image2_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

    Published 2023
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  12. 12
    Image3_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png

    Image3_Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.png by Simon Chasles (16927875)

    Published 2023
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  13. 13
    Fig 1 -

    Fig 1 - by Dillion M. Fox (4043159)

    Published 2022
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  14. 14
    Fig 2 -

    Fig 2 - by Dillion M. Fox (4043159)

    Published 2022
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  15. 15
    DataSheet2_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF

    DataSheet2_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF by András Micsonai (801004)

    Published 2022
    “…The mathematical analysis applies the k-nearest neighbor algorithm with cosine distance function, which is independent of the spectral amplitude and thus free of concentration determination errors. …”
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  16. 16
    DataSheet3_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.xlsx

    DataSheet3_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.xlsx by András Micsonai (801004)

    Published 2022
    “…The mathematical analysis applies the k-nearest neighbor algorithm with cosine distance function, which is independent of the spectral amplitude and thus free of concentration determination errors. …”
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  17. 17
    DataSheet1_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF

    DataSheet1_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF by András Micsonai (801004)

    Published 2022
    “…The mathematical analysis applies the k-nearest neighbor algorithm with cosine distance function, which is independent of the spectral amplitude and thus free of concentration determination errors. …”
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  18. 18
    the functioning of BRPSO.

    the functioning of BRPSO. by Hossein Jarrahi (22530251)

    Published 2025
    “…The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
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  19. 19
    Characteristic of 6- and 10-story SMRF [99,98].

    Characteristic of 6- and 10-story SMRF [99,98]. by Hossein Jarrahi (22530251)

    Published 2025
    “…The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
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  20. 20
    The RFD’s behavior mechanism (2002).

    The RFD’s behavior mechanism (2002). by Hossein Jarrahi (22530251)

    Published 2025
    “…The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
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