يعرض 121 - 140 نتائج من 205 نتيجة بحث عن '(( binary based model optimization algorithm ) OR ( genes based complex optimization algorithm ))', وقت الاستعلام: 1.63s تنقيح النتائج
  1. 121

    The architecture of ILSTM. حسب Asmaa Ahmed Awad (16726315)

    منشور في 2023
    "…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …"
  2. 122

    Parameter setting for LSTM. حسب Asmaa Ahmed Awad (16726315)

    منشور في 2023
    "…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …"
  3. 123

    LITNET-2020 data splitting approach. حسب Asmaa Ahmed Awad (16726315)

    منشور في 2023
    "…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …"
  4. 124

    Transformation of symbolic features in NSL-KDD. حسب Asmaa Ahmed Awad (16726315)

    منشور في 2023
    "…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …"
  5. 125
  6. 126

    DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx حسب Yuhong Huang (115702)

    منشور في 2021
    "…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …"
  7. 127

    Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX حسب Umesh C. Sharma (10785063)

    منشور في 2021
    "…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …"
  8. 128

    Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx حسب Changjiang Liang (21099887)

    منشور في 2025
    "…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…"
  9. 129

    Table2_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX حسب Jianwei Li (135213)

    منشور في 2024
    "…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …"
  10. 130

    Table3_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX حسب Jianwei Li (135213)

    منشور في 2024
    "…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …"
  11. 131

    Table1_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX حسب Jianwei Li (135213)

    منشور في 2024
    "…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …"
  12. 132

    Table5_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX حسب Jianwei Li (135213)

    منشور في 2024
    "…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …"
  13. 133

    Table6_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX حسب Jianwei Li (135213)

    منشور في 2024
    "…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …"
  14. 134

    Table4_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX حسب Jianwei Li (135213)

    منشور في 2024
    "…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …"
  15. 135

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

    منشور في 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. …"
  16. 136

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

    منشور في 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. …"
  17. 137

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

    منشور في 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. …"
  18. 138

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

    منشور في 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. …"
  19. 139

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

    منشور في 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. …"
  20. 140

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

    منشور في 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. …"