Showing 161 - 180 results of 329 for search '(( binary a based optimization algorithm ) OR ( primary data processing optimization algorithm ))*', query time: 0.71s Refine Results
  1. 161

    SHAP bar plot. by Meng Cao (105914)

    Published 2025
    “…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
  2. 162

    Sample screening flowchart. by Meng Cao (105914)

    Published 2025
    “…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
  3. 163

    Descriptive statistics for variables. by Meng Cao (105914)

    Published 2025
    “…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
  4. 164

    SHAP summary plot. by Meng Cao (105914)

    Published 2025
    “…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
  5. 165

    ROC curves for the test set of four models. by Meng Cao (105914)

    Published 2025
    “…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
  6. 166

    Display of the web prediction interface. by Meng Cao (105914)

    Published 2025
    “…</p><p>Objective</p><p>This study aimed to develop a risk prediction model for CI in CKD patients using machine learning algorithms, with the objective of enhancing risk prediction accuracy and facilitating early intervention.…”
  7. 167

    Dendrogram of the stock prices. by Muhammad Hilal Alkhudaydi (21560690)

    Published 2025
    “…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”
  8. 168

    Descriptive statistics on stock prices. by Muhammad Hilal Alkhudaydi (21560690)

    Published 2025
    “…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”
  9. 169

    Correlation heatmap of the principal components. by Muhammad Hilal Alkhudaydi (21560690)

    Published 2025
    “…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”
  10. 170

    Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)

    Published 2019
    “…However, extraction of trace-abundance signals from complex data sets (<i>m</i>/<i>z</i> value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. …”
  11. 171

    Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)

    Published 2019
    “…However, extraction of trace-abundance signals from complex data sets (<i>m</i>/<i>z</i> value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. …”
  12. 172

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
  13. 173

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
  14. 174

    Training losses for N = 10. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
  15. 175

    Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
  16. 176

    Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

    Published 2025
    “…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”
  17. 177

    Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

    Published 2019
    “…<div><p>An image classification algorithm based on adaptive feature weight updating is proposed to address the low classification accuracy of the current single-feature classification algorithms and simple multifeature fusion algorithms. …”
  18. 178

    Proposed reinforcement learning architecture. by Enoch Solomon (21416703)

    Published 2025
    “…<div><p>In the realm of game playing, deep reinforcement learning predominantly relies on visual input to map states to actions. The visual data extracted from the game environment serves as the primary foundation for state representation in reinforcement learning agents. …”
  19. 179

    Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

    Published 2021
    “…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”
  20. 180

    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. …”