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Showing 1 - 14 results of 14 for search '(( binary pairs based optimization algorithm ) OR ( final target dose optimization algorithm ))', query time: 0.73s Refine Results
  1. 1
    Model results for daily COVID-19 caused deaths after every step of the iterative optimization algorithm.

    Model results for daily COVID-19 caused deaths after every step of the iterative optimization algorithm. by Martin Bicher (10852788)

    Published 2022
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  2. 2
    Model results for daily COVID-19 caused deaths for simulations performed during the second step of the optimization algorithm.

    Model results for daily COVID-19 caused deaths for simulations performed during the second step of the optimization algorithm. by Martin Bicher (10852788)

    Published 2022
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  3. 3
    Accumulated model results for daily COVID-19 caused deaths for all the simulations performed during the iterative optimization.

    Accumulated model results for daily COVID-19 caused deaths for all the simulations performed during the iterative optimization. by Martin Bicher (10852788)

    Published 2022
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  4. 4
    Table_1_Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy.pdf

    Table_1_Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy.pdf by Kailyn Stenhouse (10259801)

    Published 2021
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  5. 5
    Table_1_Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy.pdf

    Table_1_Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy.pdf by Kailyn Stenhouse (10259801)

    Published 2021
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  6. 6
    <i>hi</i>PRS algorithm process flow.

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
    “…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i><sub><i>δ</i></sub>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i><sub><i>δ</i></sub>, <i>hi</i>PRS constructs <i>I</i><sub><i>K</i></sub>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …”
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  7. 7
    DataSheet_1_Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.docx

    DataSheet_1_Predicting Lapatinib Dose Regimen Using Machine Learning and Deep Learning Techniques Based on a Real-World Study.docx by Ze Yu (1783576)

    Published 2022
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  8. 8
    Symmetry test result.

    Symmetry test result. by Jiyong Moon (16819302)

    Published 2023
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  9. 9
    Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf

    Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

    Published 2020
    “…And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”
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  10. 10
    Methodological scheme for achieving - A study of what exactly is the reason for the long-term use of toxic amygdalin in the social group

    Methodological scheme for achieving - A study of what exactly is the reason for the long-term use of toxic amygdalin in the social group by Vasil Tsanov (16788891)

    Published 2023
    “…<p><b>article</b> - <b>DOI:</b> <a href="http://dx.doi.org/10.2174/1871520620666200313163801" rel="noreferrer" target="_blank">10.2174/1871520620666200313163801</a><br><b>monograph</b> - <b>DOI:</b> <a href="https://doi.org/10.5281/zenodo.7295357" rel="noreferrer" target="_blank">10.5281/zenodo.7295357</a> | ISBN: 978-619-91534-4-4<br><b>ATLAS</b><b> </b>- presentation of the finale information in the most applied and convenient form of application - <b>DOI:</b> <a href="https://doi.org/10.5281/zenodo.8009390" rel="noreferrer" target="_blank">10.5281/zenodo.8009390</a> | ISBN: 978-619-91534-5-1</p><p>... …”
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  11. 11
    DataSheet_2_An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.xlsx

    DataSheet_2_An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.xlsx by Hanlin Wang (240998)

    Published 2023
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  12. 12
    DataSheet_1_An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.xlsx

    DataSheet_1_An integrated solution of deep reinforcement learning for automatic IMRT treatment planning in non-small-cell lung cancer.xlsx by Hanlin Wang (240998)

    Published 2023
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  13. 13
    Presentation_1_Finite Element-Based Personalized Simulation of Duodenal Hydrogel Spacer: Spacer Location Dependent Duodenal Sparing and a Decision Support System for Spacer-Enabled Pancreatic Cancer Radiation Therapy.pptx

    Presentation_1_Finite Element-Based Personalized Simulation of Duodenal Hydrogel Spacer: Spacer Location Dependent Duodenal Sparing and a Decision Support System for Spacer-Enabled... by Hamed Hooshangnejad (12282518)

    Published 2022
    “…Next, stereotactic body radiation therapy (SBRT) plans were designed and dosimetrically analyzed. Finally, in the prediction phase, using the result of the simulation phase, we created a Bayesian DSS to predict the optimal spacer location and biological effective dose (BED).…”
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  14. 14
    Seed mix selection model

    Seed mix selection model by Bethanne Bruninga-Socolar (10923639)

    Published 2022
    “…The model thus requires three types of data presented as matrices in order to calculate the maximum number of bee species supported by a given seed mix: 1) adult phenology of each bee species, where each cell represents whether or not that bee species was observed in the data during a given time period, 2) flowering phenology of plants, where each cell represents whether or not a bee was collected from that plant species during a given time period, and 3) pairwise interactions between plant species and bee species, where each cell represents whether each plant-bee species pair was observed interacting in the data.</p> <p>  </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …”
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