Search alternatives:
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
driven optimization » design optimization (Expand Search), dose optimization (Expand Search), process optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
driven optimization » design optimization (Expand Search), dose optimization (Expand Search), process optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
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Parameter settings.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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Dynamic resource allocation process.
Published 2025“…Subsequently, we implement an optimal binary tree decision-making algorithm, grounded in dynamic programming, to achieve precise allocation of elastic resources within data streams, significantly bolstering resource utilization. …”
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23
Thesis-RAMIS-Figs_Slides
Published 2024“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”
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24
Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png
Published 2025“…RSEE projects heterogeneous input data into an exertion-conditioned latent space, aligning model predictions with observed physiological variance and mitigating false positives by explicitly modeling the overlap between athletic remodeling and subclinical pathology.…”