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cnn software  (MathWorks Inc)


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    MathWorks Inc cnn software
    Cnn Software, 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/cnn+software/10__1016_slash_j__jddst__2024__106424-218-8-12
    Average 90 stars, based on 1 article reviews
    cnn software - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: C. elegans molting requires rhythmic accumulation of the Grainyhead/ LSF transcription factor GRH ‐1
    Article Snippet: I would expect the authors to provide a github public link with the scripts to perform the RNA-seq and ChIP-seq analyses, the Matlab scripts to analyze bioluminescence experiments (Fig. 2,5,7) and the CNN used to analyze single worm molting, even if the method is to be described elsewhere.

    Article Title: Deep Learning in Selected Cancers’ Image Analysis—A Survey
    Article Snippet: A CNN developed in Matlab R2018a (The MathWorks) was employed for the tumor classification.

    Article Title: Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network
    Article Snippet: The CNN is implemented and optimized in MATLAB.

    Article Title: The Use of Optical Coherence Tomography and Convolutional Neural Networks to Distinguish Normal and Abnormal Oral Mucosa
    Article Snippet: The CNN was loaded into MATLAB as an object comprising a series of layers.

    Article Title: Prediction of drug dissolution from sustained-release pellet by a portable near-infrared spectrometer
    Article Snippet: Dissolution properties are critical assessment targets to be examined in the development of sustained-release pellets.. Comprehensively understanding the dissolution behavior of the drug, followed by appropriate formulation adjustment, is essential to enhance the product quality consistency.. In order to achieve the rapid prediction of the dissolution behavior of the sinomenine hydrochloride (SH) sustained-release pellets, a method based on the portable near-infrared (NIR) spectrometer combined with machine learning was proposed.

    Article Title: Data-driven analytics and modelling of circular supply chains for net zero manufacturing
    Article Snippet: This study aims to explore the application of data-driven analytics and modelling, using Convolutional Neural Networks (CNN) and MATLAB, to develop circular supply chains that support net zero manufacturing.. As industries face growing pressure to reduce their environmental impact, circular supply chains, which focus on resource reuse, waste reduction, and sustainable production, are becoming essential.. By integrating CNN models for data analysis and optimization, this research enhances the ability to identify inefficiencies, forecast demand, and optimize resource flows, contributing to a reduction in carbon emissions.

    Software:

    Article Title: Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network
    Article Snippet: .. Convolutional neural network—import dataset The CNN software used in this study was MATLAB R2018a. .. First, AF and NSR ECG images were loaded into MATLAB, and then all images were divided into training and test sets.

    Article Title: Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network
    Article Snippet: .. The CNN software used in this study was MATLAB R2018a. .. First, AF and NSR ECG images were loaded into MATLAB, and then all images were divided into training and test sets.



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    Schematic description of the initial (‘out-of-box’) <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN)</t> used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.
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    Image Search Results


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    Article Title: Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review

    doi: 10.3390/medicina61040737

    Figure Lengend Snippet: Papers on AI tools applied to diagnoses of knee conditions.

    Article Snippet: Hoffmann et al. [ ] tested the performance of a CNN implemented in a common planning software (mediCAD ® 7.0; mediCAD Hectec GmbH) that allows analysis and preoperative planning.

    Techniques: Imaging, Diagnostic Assay, Sequencing, Software, Labeling, Biomarker Discovery, Magnetic Resonance Imaging

    Papers reviewed on AI tools applied to surgical planning.

    Journal: Medicina

    Article Title: Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review

    doi: 10.3390/medicina61040737

    Figure Lengend Snippet: Papers reviewed on AI tools applied to surgical planning.

    Article Snippet: Hoffmann et al. [ ] tested the performance of a CNN implemented in a common planning software (mediCAD ® 7.0; mediCAD Hectec GmbH) that allows analysis and preoperative planning.

    Techniques: Imaging, Diagnostic Assay, Software, Immunocytochemistry, Generated, Functional Assay, Biomarker Discovery

    Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

    Journal: BJA Open

    Article Title: Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study ☆

    doi: 10.1016/j.bjao.2023.100236

    Figure Lengend Snippet: Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

    Article Snippet: A CNN in RStudio was built based on a published example of code used to process the Modified National Institute of Standards and Technology (MNIST) image dataset of handwritten digits ( ).

    Techniques: Modification, Muscles, Activation Assay