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scovnet classification 18-layered  (Kaggle Inc)

 
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    Kaggle Inc scovnet classification 18-layered
    Various parameters for hyper-tuning of the proposed <t> SCovNet. </t>
    Scovnet Classification 18 Layered, supplied by Kaggle 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/scovnet+classification+18-layered/scovnet+classification+18+layered/pmc09928742-299-3-13
    Average 90 stars, based on 1 article reviews
    scovnet classification 18-layered - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19"

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    Journal: Biocybernetics and Biomedical Engineering

    doi: 10.1016/j.bbe.2023.01.005

    Various parameters for hyper-tuning of the proposed SCovNet.
    Figure Legend Snippet: Various parameters for hyper-tuning of the proposed SCovNet.

    Techniques Used: Shear

    Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.
    Figure Legend Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques Used:

    Performance parameters (Kaggle database).
    Figure Legend Snippet: Performance parameters (Kaggle database).

    Techniques Used:

    Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for GitHub database respectively.
    Figure Legend Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for GitHub database respectively.

    Techniques Used:

    Performance parameters (GitHub database).
    Figure Legend Snippet: Performance parameters (GitHub database).

    Techniques Used:

    A curves of: (a) Receiver operating characteristic (ROC) for the proposed SCovNet model; (b) Precision-Recall curve of the proposed SCovNet deep learning model.
    Figure Legend Snippet: A curves of: (a) Receiver operating characteristic (ROC) for the proposed SCovNet model; (b) Precision-Recall curve of the proposed SCovNet deep learning model.

    Techniques Used:

    Ablation Study of the proposed SCovNet on Git-Hub database
    Figure Legend Snippet: Ablation Study of the proposed SCovNet on Git-Hub database

    Techniques Used:

    Performance of the proposed work with other similar studies.
    Figure Legend Snippet: Performance of the proposed work with other similar studies.

    Techniques Used:

    Related Articles

    Shear:

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19
    Article Snippet: associated performance parameters are reported in . .. Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively. .. The performance of the different layered SCovNet model



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    scovnet classification 18-layered  (Kaggle Inc)
    90
    Kaggle Inc scovnet classification 18-layered
    Various parameters for hyper-tuning of the proposed <t> SCovNet. </t>
    Scovnet Classification 18 Layered, supplied by Kaggle 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/scovnet+classification+18-layered/scovnet+classification+18+layered/pmc09928742-299-3-13
    Average 90 stars, based on 1 article reviews
    scovnet classification 18-layered - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

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    Various parameters for hyper-tuning of the proposed SCovNet.

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Various parameters for hyper-tuning of the proposed SCovNet.

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques: Shear

    Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques:

    Performance parameters (Kaggle database).

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Performance parameters (Kaggle database).

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques:

    Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for GitHub database respectively.

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for GitHub database respectively.

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques:

    Performance parameters (GitHub database).

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Performance parameters (GitHub database).

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques:

    A curves of: (a) Receiver operating characteristic (ROC) for the proposed SCovNet model; (b) Precision-Recall curve of the proposed SCovNet deep learning model.

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: A curves of: (a) Receiver operating characteristic (ROC) for the proposed SCovNet model; (b) Precision-Recall curve of the proposed SCovNet deep learning model.

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques:

    Ablation Study of the proposed SCovNet on Git-Hub database

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Ablation Study of the proposed SCovNet on Git-Hub database

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques:

    Performance of the proposed work with other similar studies.

    Journal: Biocybernetics and Biomedical Engineering

    Article Title: SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

    doi: 10.1016/j.bbe.2023.01.005

    Figure Lengend Snippet: Performance of the proposed work with other similar studies.

    Article Snippet: Confusion matrices of SCovNet Classification: (a) 18-layered; (b) 50-layered; (c) 101-layered for the Kaggle database, respectively.

    Techniques: