Showing 1 - 20 results of 26 for search '(( lines based wolf optimization algorithm ) OR ( binary _ common optimization algorithm ))', query time: 0.48s Refine Results
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    Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

    Published 2025
    “…The algorithm was applied to aqueous, binary mixture systems composed of 37 common biochemical substances such as amino acids, organic acids, and sugars. …”
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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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    DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf by Marcel Dahms (9160118)

    Published 2022
    “…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…”
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    MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

    Published 2025
    “…Currently, classical high-performance and parallel spatial computing architectures are commonly employed to solve geospatial optimization problems. …”
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    Generalized Tensor Decomposition With Features on Multiple Modes by Jiaxin Hu (1327875)

    Published 2021
    “…An efficient alternating optimization algorithm with provable spectral initialization is further developed. …”
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    Contextual Dynamic Pricing with Strategic Buyers by Pangpang Liu (18886419)

    Published 2024
    “…This underscores the rate optimality of our policy. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. …”
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    Bayesian sequential design for sensitivity experiments with hybrid responses by Yuxia Liu (1779592)

    Published 2023
    “…<p>In experimental design, a common problem seen in practice is when the result includes one binary response and multiple continuous responses. …”
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    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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    Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX by Orit Mazza (12081914)

    Published 2022
    “…Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10–30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. …”
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    Supplementary Material 8 by Nishitha R Kumar (19750617)

    Published 2025
    “…</li><li><b>XGboost: </b>An optimized gradient boosting algorithm that efficiently handles large genomic datasets, commonly used for high-accuracy predictions in <i>E. coli</i> classification.…”
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