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Showing 1 - 20 results of 40 for search '(( genes based codon optimization algorithm ) OR ( binary cases based optimization algorithm ))', query time: 0.52s Refine Results
  1. 1
    Comparison between ILSTM and machine learning-based algorithms in binary classification.

    Comparison between ILSTM and machine learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

    Published 2023
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  2. 2
    Comparison between ILSTM and deep learning-based algorithms in binary classification.

    Comparison between ILSTM and deep learning-based algorithms in binary classification. by Asmaa Ahmed Awad (16726315)

    Published 2023
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  3. 3
    MSE for ILSTM algorithm in binary classification.

    MSE for ILSTM algorithm in binary classification. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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  4. 4
    Several loop patterns found in binary code.

    Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

    Published 2020
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  5. 5
    Table5_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table5_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  6. 6
    Image2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  7. 7
    Table1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  8. 8
    Image1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image1_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  9. 9
    Image3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  10. 10
    Table3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table3_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  11. 11
    Table4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  12. 12
    Table2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX

    Table2_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.XLSX by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  13. 13
    Image4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG

    Image4_gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm.PNG by Ali Mostafa Anwar (7454504)

    Published 2023
    “…For instance, generated S<sub>ij</sub> weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. …”
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  14. 14
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
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  15. 15
    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans.

    Random forest model performs better than support vector machine algorithms and when it primarily uses spontaneous photopic ERG of 60-s duration in humans. by Ramsés Noguez Imm (6658637)

    Published 2023
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  16. 16
    Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabetes model.

    Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)

    Published 2023
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  17. 17
    UK Biobank data wrangling statistics and results.

    UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

    Published 2020
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  18. 18
    Optimal Timings Codebook.xlsx

    Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

    Published 2021
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  19. 19
    Summary of LITNET-2020 dataset.

    Summary of LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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  20. 20
    SHAP analysis for LITNET-2020 dataset.

    SHAP analysis for LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

    Published 2023
    “…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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