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machine learning toolbox in matlab  (MathWorks Inc)


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    MathWorks Inc machine learning toolbox in matlab
    Machine Learning Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2368 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+function+ksdensity/Statistics+and+Machine+Learning+Toolbox/pmc07612017-254-26-30
    Average 96 stars, based on 2368 article reviews
    machine learning toolbox in matlab - by Bioz Stars, 2026-11
    96/100 stars

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    Related Articles

    Comparison:

    Article Title: Experimental evaluation, backpropagation neural network prediction and bibliometric analysis of bioelectricity production from agricultural wastes by microbial fuel cell.
    Article Snippet: In this study, a two-stage bioprocess combining enzymatic hydrolysis and a 4 microbial fuel cell (MFC) was established to evaluate electricity generation from three 5 agricultural wastes (mushroom sticks, rice husks and bamboo shavings).. The highest 6 glucose concentration of 4 g/L was obtained in the mushroom sticks hydrolysate.. 7 However, rice husk hydrolysate produced the highest power density of 26.02 mW/m at 8 a current density of 29.45 mA/m.

    Sensitivity:

    Article Title: Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation.
    Article Snippet: .. The linear mixed-effects sensitivity analysis described below was fitted in MATLAB R2024a with fitlme (Statistics and Machine Learning Toolbox version 24.1), so the statistical analysis reported here used both environments. ..

    Analysis:

    Article Title: Longitudinal Multi-Batch Characterisation of Beer Wort Fermentation Foam Using Image-Based Semantic Segmentation.
    Article Snippet: .. The linear mixed-effects sensitivity analysis described below was fitted in MATLAB R2024a with fitlme (Statistics and Machine Learning Toolbox version 24.1), so the statistical analysis reported here used both environments. ..

    Article Title: An amygdala-mediated simulation-to-decision cascade aligns interbrain dynamics to solve social dilemmas.
    Article Snippet: Mutual cooperation (MC) requires independent decision-makers to coordinate under uncertainty about each other’s intentions.. Using simultaneous intracranial electroencephalography from epilepsy–patient dyads playing a real-time iterated prisoner’s dilemma, we identified neurocognitive mechanisms that enable interacting brains to align during mutual cooperation.. Amygdala interbrain representational alignment (IBRA), similarity in fine-grained neural spectral patterns across partners, emerged selectively during MC and predicted trial-by-trial outcomes.

    other:

    Article Title: Feature-Based Machine-Learning Spike Marking in Signal and Source Space EEG: A Proof-of-Principle Case Study of Focal Epilepsy.
    Article Snippet: All analyses were performed in MATLAB R2025b using FieldTrip (https://www.fieldtriptoolbox.org) [32], the Signal Processing Toolbox (accessed on 2 September 2026) (https://www.mathworks.com/products/ signal.html), the Statistics and Machine Learning Toolbox (accessed on 2 September 2026) (https://www.mathworks.com/products/statistics.html) and some custom scripts.



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    Image Search Results


    “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Journal: iScience

    Article Title: Supplementary motor area in speech initiation: A large-scale intracranial EEG evaluation of stereotyped word articulation

    doi: 10.1016/j.isci.2024.111531

    Figure Lengend Snippet: “Scrambled” response time and articulatory variability (A and B) Mean (A) and standard deviation (B) of response time (RT) distributions for all subjects and separately for sEEG implants only, and SDE only. Each data point represents the mean or standard deviation of RT for a single subject. Box-and-whisker plots indicate median, upper and lower quartiles, and extent of outliers. Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production. Time-zero is word onset as manually labeled by inspecting the spectrogram. (D) Probability distribution function of phoneme offsets color-coded from phonemes in (C). These distributions were generated from a subset of subjects ( n = 40) with sufficiently clear audio for automated phonemic transcription.

    Article Snippet: Half-violin plots depict probability density estimates for each dataset using default parameters of the ksdensity() MATLAB function. (C) Spectrogram and corresponding phoneme boundaries identified by the Montreal Forced Aligner (McAuliffe et al., 2017) for one “scrambled” production.

    Techniques: Standard Deviation, Whisker Assay, Labeling, Generated