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Showing 1 - 20 results of 93 for search '(( linear range based optimization algorithm ) OR ( binary data process optimisation algorithm ))', query time: 0.59s Refine Results
  1. 1
    Analysis and design of algorithms for the manufacturing process of integrated circuits

    Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

    Published 2023
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  2. 2
    Previously literatures on feature evaluation for relevance based on linear transformation.

    Previously literatures on feature evaluation for relevance based on linear transformation. by Jia Liu (41593)

    Published 2025
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  3. 3
    Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.

    Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

    Published 2024
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  4. 4
    Category range of secondary indicators.

    Category range of secondary indicators. by Yixuan Lu (9987980)

    Published 2023
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  5. 5
    The linearly decreasing convergence factor a.

    The linearly decreasing convergence factor a. by Junhao Wei (6816803)

    Published 2025
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  6. 6
    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(1,1), i.e., uniform distribution over the interval, according to their respective physical ranges.

    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(1,1), i.e., uniform distribution over the interval, according to thei... by Luan Carlos de Sena Monteiro Ozelim (16914117)

    Published 2023
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  7. 7
    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(2,2), i.e., the center of mass of the distribution at the center of the interval, according to their respective physical ranges.

    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(2,2), i.e., the center of mass of the distribution at the center of t... by Luan Carlos de Sena Monteiro Ozelim (16914117)

    Published 2023
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  8. 8
    Optimized parameters and accuracies of the automatic speaker identification on all speech data based on the four supervised learning algorithms [i.e., linear discriminant analysis (LDA), naïve bayes classifier (NBC), random forest (RF), and support vector machine (SVM)].

    Optimized parameters and accuracies of the automatic speaker identification on all speech data based on the four supervised learning algorithms [i.e., linear discriminant analysis... by Puyang Geng (14883291)

    Published 2023
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  9. 9
    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(5,2), i.e., the center of mass of the distribution closer to the right end of the interval, according to their respective physical ranges.

    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(5,2), i.e., the center of mass of the distribution closer to the righ... by Luan Carlos de Sena Monteiro Ozelim (16914117)

    Published 2023
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  10. 10
    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(2,5), i.e., the center of mass of the distribution closer to the left end of the interval, according to their respective physical ranges.

    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(2,5), i.e., the center of mass of the distribution closer to the left... by Luan Carlos de Sena Monteiro Ozelim (16914117)

    Published 2023
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  11. 11
    Linear-Scaling Local Natural Orbital CCSD(T) Approach for Open-Shell Systems: Algorithms, Benchmarks, and Large-Scale Applications

    Linear-Scaling Local Natural Orbital CCSD(T) Approach for Open-Shell Systems: Algorithms, Benchmarks, and Large-Scale Applications by P. Bernát Szabó (10526316)

    Published 2023
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  12. 12
    Optimized parameters and accuracies of the automatic classification analyses on speech with and without face mask across language based on the four supervised learning algorithms [i.e., linear discriminant analysis (LDA), naïve bayes classifier (NBC), random forest (RF), and support vector machine (SVM)].

    Optimized parameters and accuracies of the automatic classification analyses on speech with and without face mask across language based on the four supervised learning algorithms [... by Puyang Geng (14883291)

    Published 2023
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  13. 13
    Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis.

    Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis. by Richard Porwancher (11413931)

    Published 2021
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  14. 14
    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(0.5,0.5), i.e., two centers of mass for the distribution located at both the right and left ends of the interval, according to their respective physical ranges.

    Prediction intervals of probabilities of failure considering that the input variables are linearly scaled Beta(0.5,0.5), i.e., two centers of mass for the distribution located at b... by Luan Carlos de Sena Monteiro Ozelim (16914117)

    Published 2023
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  15. 15
    A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range.

    A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. by Jamison H. Burks (21029607)

    Published 2025
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  16. 16
    Aggregated actual vs. predicted power output across all ML models.

    Aggregated actual vs. predicted power output across all ML models. by Montaser Abdelsattar (20606102)

    Published 2025
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  17. 17
    Research objectives for enhancing WT power output prediction using ML and DL models.

    Research objectives for enhancing WT power output prediction using ML and DL models. by Montaser Abdelsattar (20606102)

    Published 2025
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  18. 18
    RMSE values for ML models in WT power prediction.

    RMSE values for ML models in WT power prediction. by Montaser Abdelsattar (20606102)

    Published 2025
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  19. 19
    Overview of ML model selection and training for WT power output prediction.

    Overview of ML model selection and training for WT power output prediction. by Montaser Abdelsattar (20606102)

    Published 2025
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  20. 20
    Fig 8 -

    Fig 8 - by Montaser Abdelsattar (20606102)

    Published 2025
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