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Showing 1 - 20 results of 23 for search '(( algorithms decision function ) OR ( algorithm python function ))~', query time: 0.32s Refine Results
  1. 1
    Comparison of algorithms in two cases.

    Comparison of algorithms in two cases. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  2. 2
    Flow of the NSGA-II algorithm.

    Flow of the NSGA-II algorithm. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  3. 3
    The optimal solution set of NYN by using different algorithms.

    The optimal solution set of NYN by using different algorithms. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  4. 4
    The optimal solution set of HN by using different algorithms.

    The optimal solution set of HN by using different algorithms. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  5. 5
    Information of nodes and pipes of NYN.

    Information of nodes and pipes of NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  6. 6
    The topology of the NYN.

    The topology of the NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  7. 7
    The result of Wilcoxon signed-rand test.

    The result of Wilcoxon signed-rand test. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  8. 8
    The Simulation and optimization process of pipe diameter selection.

    The Simulation and optimization process of pipe diameter selection. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  9. 9
    Optional pipe diameter and unit price of NYN.

    Optional pipe diameter and unit price of NYN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  10. 10
    Optional pipe diameter and unit price of HN.

    Optional pipe diameter and unit price of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  11. 11
    The topology of HN.

    The topology of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  12. 12
    Information of nodes and pipes of HN.

    Information of nodes and pipes of HN. by Yi Tao (178829)

    Published 2022
    Subjects: “…evolutionary genetic algorithm…”
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  13. 13
    <b>Algorithm Pseudocode</b>

    <b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

    Published 2025
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  14. 14
    Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF

    Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

    Published 2024
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  15. 15
    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways.

    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

    Published 2021
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  16. 16
    LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery

    LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

    Published 2025
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  17. 17
    Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making

    Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

    Published 2025
    “…In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. …”
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  18. 18
    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043)

    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

    Published 2025
    “…The described extracted features were used to predict leaf betalain content (µg per FW) using multiple machine learning regression algorithms (Linear regression, Ridge regression, Gradient boosting, Decision tree, Random forest and Support vector machine) using the <i>Scikit-learn</i> 1.2.1 library in Python (v.3.10.1) (list of hyperparameters used is given in <a href="#sup1" target="_blank">Supplementary Data S5</a>). …”
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  19. 19
    Map Matching on Low Sampling Rate Trajectories Through Deep Inverse Reinforcement Learning and Multi Intention Modeling

    Map Matching on Low Sampling Rate Trajectories Through Deep Inverse Reinforcement Learning and Multi Intention Modeling by Reza Safarzadeh (18072472)

    Published 2024
    “…</li><li>`value_iteration.py`: Contains the implementation of the value iteration algorithm and Q-Learning to solve RL and Markov decision processes (MDPs).…”
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  20. 20
    Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.

    Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

    Published 2025
    “…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”
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