Search alternatives:
maximization algorithm » optimization algorithms (Expand Search), classification algorithm (Expand Search)
process maximization » process optimization (Expand Search), profit maximization (Expand Search), process optimisation (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
binary ai » binary _ (Expand Search), binary pairs (Expand Search)
ai driven » _ driven (Expand Search), a driver (Expand Search)
a process » _ process (Expand Search)
binary a » binary _ (Expand Search), binary b (Expand Search), hilary a (Expand Search)
Showing 1 - 20 results of 21 for search '(( binary ai driven optimization algorithm ) OR ( binary a process maximization algorithm ))', query time: 0.55s Refine Results
  1. 1
    Confusion matrix for a binary outcome (adapted from Fawcett [30]).

    Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

    Published 2022
    Save to List
    Saved in:
  2. 2
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  3. 3
    An Example of a WPT-MEC Network.

    An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  4. 4
    Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate.

    Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. by Hend Bayoumi (22693738)

    Published 2025
    Save to List
    Saved in:
  5. 5
    Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png

    Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

    Published 2025
    “…</p>Methods<p>To address these challenges, we propose a novel AI-driven framework that incorporates two key methodological innovations: CardioSpectra, a structured sparse inference model, and Risk-Stratified Exertional Embedding (RSEE), a domain-specific representation learning strategy. …”
    Save to List
    Saved in:
  6. 6
    MCLP_quantum_annealer_V0.5

    MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

    Published 2025
    Save to List
    Saved in:
  7. 7
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  8. 8
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  9. 9
    Training losses for N = 10.

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

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  10. 10
    Comparisons between ADAM and NADAM optimizers.

    Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  11. 11
    Normalized computation rate for N = 10.

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

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  12. 12
    Summary of Notations Used in this paper.

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

    Published 2025
    “…This paper considers a wireless-powered MEC network employing a binary offloading policy, in which the computation tasks of MDs are either executed locally or fully offloaded to an edge server (ES). …”
    Save to List
    Saved in:
  13. 13
    datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf

    datasheet1_Graph Neural Networks for Maximum Constraint Satisfaction.pdf by Jan Tönshoff (10192709)

    Published 2021
    Save to List
    Saved in:
  14. 14
    Example depiction of the autoML process.

    Example depiction of the autoML process. by Cynthia Lokker (3813832)

    Published 2024
    Save to List
    Saved in:
  15. 15
    Schematic representation of semi-automated algorithm for organoids analyzing.

    Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

    Published 2025
    Save to List
    Saved in:
  16. 16
    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>.

    Optimization of tracking parameters based on <i>P</i><sub><i>swap</i></sub>. by Benjamin Gallois (10131683)

    Published 2021
    Save to List
    Saved in:
  17. 17
    Objective performance of fully-automated SLAVV software.

    Objective performance of fully-automated SLAVV software. by Samuel A. Mihelic (11539996)

    Published 2021
    Save to List
    Saved in:
  18. 18
    Performance of the model on the validation dataset of EFSI.

    Performance of the model on the validation dataset of EFSI. by Anna Demchenko (18449439)

    Published 2025
    Save to List
    Saved in:
  19. 19
    Contextual Dynamic Pricing with Strategic Buyers

    Contextual Dynamic Pricing with Strategic Buyers by Pangpang Liu (18886419)

    Published 2024
    “…<p>Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. …”
    Save to List
    Saved in:
  20. 20
    Spectral estimation of large stochastic blockmodels with discrete nodal covariates

    Spectral estimation of large stochastic blockmodels with discrete nodal covariates by Angelo Mele (10809478)

    Published 2022
    “…<p>In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. We show that a network model with discrete unobserved link heterogeneity and binary (or discrete) covariates corresponds to a stochastic blockmodel (SBM). …”
    Save to List
    Saved in:

Search Tools: