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machine learning tool box  (MathWorks Inc)


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    MathWorks Inc machine learning tool box
    Machine Learning Tool Box, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2336 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/machine+learning+tool+box/Statistics+and+Machine+Learning+Toolbox/ppr0725613-99-2-1
    Average 96 stars, based on 2336 article reviews
    machine learning tool box - by Bioz Stars, 2026-10
    96/100 stars

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    Selection:

    Article Title: Serological and cellular inflammatory signatures in end‐stage kidney disease and latent tuberculosis
    Article Snippet: .. Key immune features (signatures) that contributed to differences between cohorts were identified using the least absolute shrinkage and selection operator (LASSO) penalised regression feature selection method in MATLAB (MathWorks, Massachusetts, USA) using the statistics and machine learning tool box. ..

    Software:

    Article Title: Photovoltaic power intra‐ and day‐ahead predictions with differential learning producing <scp>PDE</scp>‐modular models based on the node L‐transform derivatives
    Article Snippet: Funding information Parallel processing of Big Data IX, Grant/Award Number: nSGS2022/12 Abstract Predictions of photovoltaic (PV) energy supply, based on data statistics of weather and PV records, are required in short-intra and day-ahead planning of PV plant operations.. Numerical weather prediction (NWP), based on physical consideration, can simulate the progress of local cloudiness, although their prognoses are usually delayed by a few hours and are not provided with the quality desired by PV operators.. Differential polynomial neural network (D-PNN) is an unconventional hybrid regression technique, based on a novel learning strategy and able to resolve some problems in modeling patterns in high-dynamical and chaotic non-deterministic systems.

    Plasmid Preparation:

    Article Title: Data driven modeling of linear motion system using learning algorithm
    Article Snippet: .. The MATLAB machine learning tool box consists of models of regression decision tree, support vector machine (SVM), and Gaussian process regression models. ..

    High Throughput Screening Assay:

    Article Title: Semi-automated thrombin dynamics applying the ST Genesia thrombin generation assay
    Article Snippet: .. Thrombin dynamics analysis was performed in Matlab (R2016a, Mathworks, Eindhoven, the Netherlands) using the Matlab “Statistics and Machine Learning Tool Box” (version 10.2, Mathworks, Eindhoven), and theMatlab “Curve Fitting Toolbox” (version 3.5.3, Mathworks, Eindhoven, the Netherlands) and to ensure a high throughput computation of thrombin dynamics parameters. ..



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    Image Search Results


    Voltage U—RMSE: D-PNN = 1.153, SMLT (Tree) = 1.118 [V], 12 inputs + THDU (both D-PNN and SMLT) in the 2nd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Voltage U—RMSE: D-PNN = 1.153, SMLT (Tree) = 1.118 [V], 12 inputs + THDU (both D-PNN and SMLT) in the 2nd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power P—RMSE: D-PNN = 0.177, SMLT (GPR) = 0.182 [kW], 12 inputs + U + PF (D-PNN), 12 inputs + PF + THDU (SMLT) in the 3rd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power P—RMSE: D-PNN = 0.177, SMLT (GPR) = 0.182 [kW], 12 inputs + U + PF (D-PNN), 12 inputs + PF + THDU (SMLT) in the 3rd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    THDU—RMSE: D-PNN = 0.429, SMLT (Tree) = 0.443 [%], 12 inputs + U (both D-PNN and SMLT) in the 2nd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: THDU—RMSE: D-PNN = 0.429, SMLT (Tree) = 0.443 [%], 12 inputs + U (both D-PNN and SMLT) in the 2nd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power Factor PF—RMSE: D-PNN = 0.0516, SMLT (GPR) = 0.0496 [], 12 inputs + Pst + U (D-PNN, 12 inputs + Pst + THDU (SMLT) in the 3rd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power Factor PF—RMSE: D-PNN = 0.0516, SMLT (GPR) = 0.0496 [], 12 inputs + Pst + U (D-PNN, 12 inputs + Pst + THDU (SMLT) in the 3rd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Pst—RMSE: D-PNN = 0.811, SMLT (EBoosT) = 0.544 [], 12 inputs + Pst (both D-PNN and SMLT) in the 2nd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Pst—RMSE: D-PNN = 0.811, SMLT (EBoosT) = 0.544 [], 12 inputs + Pst (both D-PNN and SMLT) in the 2nd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Voltage U 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Voltage U 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power P 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power P 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    THDU 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: THDU 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power Factor PF 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power Factor PF 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Pst 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Pst 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques: