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vibration sensor 2d-cnn  (SoftMax Inc)

 
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    SoftMax Inc vibration sensor 2d-cnn
    Vibration Sensor 2d Cnn, supplied by SoftMax 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/result/vibration sensor 2d-cnn/product/SoftMax Inc
    Average 90 stars, based on 1 article reviews
    vibration sensor 2d-cnn - by Bioz Stars, 2026-04
    90/100 stars

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    Image Search Results


    Inlustraions of 2D CNN and GBDT models (A) Schematic overview of the 2D CNN model. Four convolutional layers and four max pooling layers are included in the 2D CNN model. Here, the kernel size of each convolutional layer is set to be three and the pooling size is two. (B) Illustration of GBDT model with N gradient boosting trees.

    Journal: iScience

    Article Title: Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring

    doi: 10.1016/j.isci.2023.107083

    Figure Lengend Snippet: Inlustraions of 2D CNN and GBDT models (A) Schematic overview of the 2D CNN model. Four convolutional layers and four max pooling layers are included in the 2D CNN model. Here, the kernel size of each convolutional layer is set to be three and the pooling size is two. (B) Illustration of GBDT model with N gradient boosting trees.

    Article Snippet: Codes for K-mean ++, 2D CNN, 3D CNN and GBDT , This paper; Mendeley Data , https://doi.org/10.17632/995vjkhm68.1.

    Techniques:

    Comparison, testing, and training of 3D CNN (A) Bar graph showing comparison of predictive performance of the 3D CNN, 2D CNN, and GBDT algorithms via analysis of root mean squared error (RMSE) and mean absolute error (MAE) (∗p < 0.05, ∗∗p < 0.05, the Student’s t test). (B) Bar graph showing comparison of Pearson correlation coefficient (PCC) values between 3D CNN, 2D CNN, and GBDT algorithms. (C) Loss graph of ten training rounds (iterations) comparing the 3D CNN and 2D CNN for both dataset 1 and dataset 2. (D) Intraclass correlation coefficient analysis of 3D CNN’s predictive accuracy on a range of images with 0 to 70μCi radioactivity (∗p = 0.001, ∗∗p = 0.001, the F test).

    Journal: iScience

    Article Title: Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring

    doi: 10.1016/j.isci.2023.107083

    Figure Lengend Snippet: Comparison, testing, and training of 3D CNN (A) Bar graph showing comparison of predictive performance of the 3D CNN, 2D CNN, and GBDT algorithms via analysis of root mean squared error (RMSE) and mean absolute error (MAE) (∗p < 0.05, ∗∗p < 0.05, the Student’s t test). (B) Bar graph showing comparison of Pearson correlation coefficient (PCC) values between 3D CNN, 2D CNN, and GBDT algorithms. (C) Loss graph of ten training rounds (iterations) comparing the 3D CNN and 2D CNN for both dataset 1 and dataset 2. (D) Intraclass correlation coefficient analysis of 3D CNN’s predictive accuracy on a range of images with 0 to 70μCi radioactivity (∗p = 0.001, ∗∗p = 0.001, the F test).

    Article Snippet: Codes for K-mean ++, 2D CNN, 3D CNN and GBDT , This paper; Mendeley Data , https://doi.org/10.17632/995vjkhm68.1.

    Techniques: Comparison, Radioactivity

    Journal: iScience

    Article Title: Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring

    doi: 10.1016/j.isci.2023.107083

    Figure Lengend Snippet:

    Article Snippet: Codes for K-mean ++, 2D CNN, 3D CNN and GBDT , This paper; Mendeley Data , https://doi.org/10.17632/995vjkhm68.1.

    Techniques: Recombinant, Software