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Showing 1 - 20 results of 27 for search '(( binary based active optimization algorithm ) OR ( genes based ai optimization algorithm ))', query time: 0.42s Refine Results
  1. 1
    Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf

    Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

    Published 2025
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  2. 2
    Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf

    Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

    Published 2025
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  3. 3
    Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf

    Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

    Published 2025
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  4. 4
    Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png

    Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png by Valentina Di Salvatore (6296114)

    Published 2025
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  5. 5
    Image 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png

    Image 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png by Valentina Di Salvatore (6296114)

    Published 2025
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  6. 6
    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results

    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

    Published 2025
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  7. 7
    QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

    QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm by Z.Y. Algamal (5547620)

    Published 2020
    “…The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. …”
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  8. 8
    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
    “…The initial implementation of the tAI had significant flaws. 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
    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
    “…The initial implementation of the tAI had significant flaws. 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
    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
    “…The initial implementation of the tAI had significant flaws. 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
    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
    “…The initial implementation of the tAI had significant flaws. 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
    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
    “…The initial implementation of the tAI had significant flaws. 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
    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
    “…The initial implementation of the tAI had significant flaws. 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
    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
    “…The initial implementation of the tAI had significant flaws. 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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  15. 15
    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
    “…The initial implementation of the tAI had significant flaws. 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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  16. 16
    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
    “…The initial implementation of the tAI had significant flaws. 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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  17. 17
    Table 1_Epilepsy-associated CHD2 missense variants and optimization strategies for genetic diagnosis: a comparative analysis of algorithms.docx

    Table 1_Epilepsy-associated CHD2 missense variants and optimization strategies for genetic diagnosis: a comparative analysis of algorithms.docx by Yu-Jie Gu (20963948)

    Published 2025
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  18. 18
    Image 1_Epilepsy-associated CHD2 missense variants and optimization strategies for genetic diagnosis: a comparative analysis of algorithms.tif

    Image 1_Epilepsy-associated CHD2 missense variants and optimization strategies for genetic diagnosis: a comparative analysis of algorithms.tif by Yu-Jie Gu (20963948)

    Published 2025
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  19. 19
    An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%.

    An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. by Zhengdong Wang (35299)

    Published 2025
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  20. 20
    Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP

    Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

    Published 2022
    “…<p>It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. …”
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