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matlab-based supervised dl algorithms (gaussian process regression and shallow neural networks)  (MathWorks Inc)


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    MathWorks Inc matlab-based supervised dl algorithms (gaussian process regression and shallow neural networks)
    Matlab Based Supervised Dl Algorithms (Gaussian Process Regression And Shallow Neural Networks), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/neural+network+algorithm/pm40609552-278-6-3
    Average 90 stars, based on 1 article reviews
    matlab-based supervised dl algorithms (gaussian process regression and shallow neural networks) - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters
    Article Snippet: We have created a supervised, model based, batch learning, neural network algorithm with the architecture of a multilayer perceptron (MLP) using MATLAB’s Neural Network package [47].

    Article Title: Defect Classification of Reinforced Concrete Structures with Nondestructive Tests Using Statistical and Machine Learning Methods
    Article Snippet: Although there are special functions in MATLAB to define the neural network algorithm, a very specific method Imperial Competitive Algorithm (ICA) is used to write and define all parameters, exclusively.

    Article Title: Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network
    Article Snippet: The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software.

    Article Title: Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network
    Article Snippet: First, the method of calculating the weight and bias values is obtained using the neural network algorithm programmed in MATLAB software, and the input is the value of the three-axes of the IMU sensor′s gyroscope and accelerometer.

    Article Title: Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change
    Article Snippet: Data analysis was performed in MATLAB using the neural network algorithm and predictions of the system are modelled in SIMULINK platform.

    Article Title: Fault Monitoring and Diagnosis of Actuators in Electromagnetic Valve-Train Based on Neural Networks Optimization Algorithm
    Article Snippet: The main training parameters of neural network algorithm were as follows: .trainparam.max_fail = 5; % Max Confirmation Failures .trainparam.goal= 0.001;% training targetminimum error .trainParam.epochs = 200; % training times .trainParam.lr = 0.05; % learning rate .trainParam.min_grad =1e-6; % minimum performance gradient The rest of the parameters used the default values of Matlab.

    Article Title: Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change
    Article Snippet: Data analysis was performed in MATLAB® using the neural network algorithm and predictions of the system are modelled in SIMULINK® platform.

    Article Title: Predicting the settlement of Urumqi subway based on wavelet denoising and BP neural network
    Article Snippet: The monitoring data of the 1st to 45th periods are used as the learning set of the neural network, and the BP neural network algorithm is implemented by MATLAB.



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    Image Search Results


    The optimal results of included studies by the algorithm.

    Journal: Nursing & Health Sciences

    Article Title: A Systematic Review of the Accuracy of Machine Learning Models for Diagnosing Pulmonary Tuberculosis: Implications for Nursing Practice and Implementation

    doi: 10.1111/nhs.70077

    Figure Lengend Snippet: The optimal results of included studies by the algorithm.

    Article Snippet: The Gaussian‐fuzzy‐neural network (GFNN) algorithm, used in one study ( n = 1 study, 8.3%) (Mithra and Sam Emmanuel ), was effective for analyzing sputum smear microscopic images.

    Techniques: Plasmid Preparation