Showing 1 - 10 results of 10 for search '(( binary damage process optimization algorithm ) OR ( lines based convex optimization algorithm ))', query time: 0.33s Refine Results
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    Performance on GradEva. by Jamilu Yahaya (18563445)

    Published 2024
    “…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”
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    The considered test problems. by Jamilu Yahaya (18563445)

    Published 2024
    “…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”
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    Performance on FunEva. by Jamilu Yahaya (18563445)

    Published 2024
    “…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”
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    Performance on Iter. by Jamilu Yahaya (18563445)

    Published 2024
    “…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”
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    Continuation of Table 2. by Jamilu Yahaya (18563445)

    Published 2024
    “…The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. …”
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    Probability flux balances can determine biochemical rates regardless of global network dynamics. by Timon Wittenstein (12908474)

    Published 2022
    “…Although probability distributions differed greatly between the four systems, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010183#pcbi.1010183.e006" target="_blank">Eq (4)</a> could identify the functional dependence of the production rate of X<sub>3</sub> based on the numerical convex optimization algorithm detailed in the Materials & Methods. …”
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    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”