Showing 121 - 140 results of 144 for search '(( binary data based optimization algorithm ) OR ( final based proteomics optimization algorithm ))', query time: 0.73s Refine Results
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    Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

    Published 2024
    “…Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”
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    Contextual Dynamic Pricing with Strategic Buyers by Pangpang Liu (18886419)

    Published 2024
    “…This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. …”
  11. 131

    Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

    Published 2025
    “…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”
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    Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

    Published 2023
    “…To deal with the problem of complex computation involved in searching for optimal designs, fast algorithms are presented using two strategies to approximate the optimal criterion, denoted as SI-optimal design and Bayesian D-optimal design, respectively. …”
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    Table 1_Machine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension.xlsx by Dan Yang (65758)

    Published 2025
    “…Subsequently, we established a machine learning-based diagnostic model. The HAPH-associated signatures were finally validated by Quantitative PCR.…”
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    Supplementary file 1_Machine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension.docx by Dan Yang (65758)

    Published 2025
    “…Subsequently, we established a machine learning-based diagnostic model. The HAPH-associated signatures were finally validated by Quantitative PCR.…”
  16. 136

    DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Cooking data were classified into binary and multiclass variables (CT4C and CT6C). …”
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    Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

    Published 2022
    “…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”
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    Models and Dataset by M RN (9866504)

    Published 2025
    “…</p><p dir="ltr"><br></p><p dir="ltr"><b>RAO (Rao Optimization Algorithm):</b><br>RAO is a parameter-less optimization algorithm that updates solutions based on simple arithmetic operations involving the best and worst individuals in the population. …”
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    Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

    Published 2024
    “…Cooking data were classified into binary and multiclass variables (CT4C and CT6C). …”