Search alternatives:
algorithms adopted » algorithms sorted (Expand Search), algorithms reported (Expand Search), algorithms often (Expand Search)
algorithm python » algorithm within (Expand Search), algorithms within (Expand Search), algorithm both (Expand Search)
adopted function » adaptive function (Expand Search), related function (Expand Search), cortex function (Expand Search)
python function » protein function (Expand Search)
Showing 101 - 120 results of 270 for search '(( algorithm python function ) OR ( algorithms adopted function ))', query time: 0.42s Refine Results
  1. 101
    PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP.

    PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  2. 102
    PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared.

    PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  3. 103
    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></sup>Different stimuli are displayed equally. If desired by the user, some stimuli can be displayed more/less frequently by assigning them a higher/lower weight.

    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  4. 104
    The results of ICA performed using PyNoetic.

    The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  5. 105
    PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA.

    PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  6. 106
    Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task.

    Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  7. 107
    Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module.

    Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  8. 108
    A typical BCI system depicting the flow and processing of EEG data.

    A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  9. 109
    Effects of prey’s detection distance (<i>D</i><sub><i>s</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<sub><i>i</i></sub>), with agents adopting Nash equilibrium behaviors under the sensory–motor algorithm across all conditions (<i>c</i><sub><i>b</i></sub> = 0, 0.001, 0.02, 0.04, and <i>r</i> = 3.2, 3.6, 4.0).

    Effects of prey’s detection distance (<i>D</i><sub><i>s</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<su... by Hiroyuki Ichijo (13891153)

    Published 2025
    Save to List
    Saved in:
  10. 110
    Effects of predators’ detection distance (<i>D</i><sub><i>w</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs (<sub><i>i</i></sub>) with agents adopting Nash equilibrium behaviors under the sensory–motor algorithm across all conditions (<i>c</i><sub><i>b</i></sub> = 0, 0.001, 0.02, 0.04, and <i>r</i> = 3.2, 3.6, 4.0).

    Effects of predators’ detection distance (<i>D</i><sub><i>w</i></sub>) on encounter frequency (<i>E</i>), agent numbers (<i>N</i><sub><i>i</i></sub>), and Nash equilibrium payoffs... by Hiroyuki Ichijo (13891153)

    Published 2025
    Save to List
    Saved in:
  11. 111
    Contrast enhancement of digital images using dragonfly algorithm

    Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

    Published 2024
    “…Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…”
    Save to List
    Saved in:
  12. 112
    POF after 300 iterations.

    POF after 300 iterations. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  13. 113
    Optimization results of the CHPDEED system.

    Optimization results of the CHPDEED system. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  14. 114
    Comparison of POF for CHPDEED system.

    Comparison of POF for CHPDEED system. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  15. 115
    DEA process.

    DEA process. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  16. 116
    Improvements to the MDEA process.

    Improvements to the MDEA process. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  17. 117
    Parameter sensitivity analysis results.

    Parameter sensitivity analysis results. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  18. 118
    Steps for obtaining a complete Pareto frontier.

    Steps for obtaining a complete Pareto frontier. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  19. 119
    CHP system architecture.

    CHP system architecture. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:
  20. 120
    POF after 150 iterations.

    POF after 150 iterations. by Tao Dong (15551)

    Published 2025
    “…The results revealed that the improved multi-objective differential evolution algorithm in Zitzler-Deb-Thiele 1 function test had generational distance of 0.0513, inverted generational distance of 0.3265, and hyper volume metric of 0.1301. …”
    Save to List
    Saved in:

Search Tools: