Showing 121 - 140 results of 176 for search '(( binary ai design optimization algorithm ) OR ( primary data processing optimization algorithm ))', query time: 0.25s Refine Results
  1. 121
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    Supporting data for “The role of forest composition heterogeneity on temperate ecosystem carbon dynamic under climate change" by Ziyu Lin (9151064)

    Published 2025
    “…The process includes (1) harmonizing Landsat 5, 7, 8, and Sentinel-2 data using the HLS algorithm, and (2) filling temporal gaps with an optimized object-based STARFM fusion algorithm. …”
  3. 123
  4. 124

    Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

    Published 2024
    “…It also incorporates climate change responses, adjust decomposition rates based on climate and environmental changes, and lead to robust estimates under different climatic scenarios. The simulation process of the DLCM involves initializing SOC stocks with spatially detailed baseline data, adding organic matter inputs based on vegetation production, and simulating microbial decomposition while adjusting for climate variables such as temperature and soil moisture. …”
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  6. 126

    Proposed method approach. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  7. 127

    LSTM model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  8. 128

    Descriptive statistics. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  9. 129

    CNN-LSTM Model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  10. 130

    MLP Model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  11. 131

    RNN Model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  12. 132

    CNN Model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  13. 133

    Bi-directional LSTM Model performance. by Muhammad Usman Tariq (11022141)

    Published 2024
    “…These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. …”
  14. 134

    Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra (17387852)

    Published 2025
    “…The proposed framework leverages a strong categorical boosting (CatBoost) algorithm optimized using Grid Search Optimization (GSO). …”
  15. 135

    Minimal Dateset. by Hongwei Yue (574068)

    Published 2025
    “…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
  16. 136

    Loss Function Comparison. by Hongwei Yue (574068)

    Published 2025
    “…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
  17. 137

    Comparative Results of Different Models. by Hongwei Yue (574068)

    Published 2025
    “…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
  18. 138

    Loss Function Comparison. by Hongwei Yue (574068)

    Published 2025
    “…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
  19. 139

    Overall Framework of the PSO-KM Model. by Hongwei Yue (574068)

    Published 2025
    “…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”
  20. 140

    Overall Framework of the PSO-KM Model. by Hongwei Yue (574068)

    Published 2025
    “…To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. …”