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base cnn  (Kaggle Inc)

 
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    Structured Review

    Kaggle Inc base cnn
    <t>Base</t> <t>CNN</t> model architecture.
    Base Cnn, 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/result/base cnn/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    base cnn - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model"

    Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

    Journal: Food Science & Nutrition

    doi: 10.1002/fsn3.70513

    Base CNN model architecture.
    Figure Legend Snippet: Base CNN model architecture.

    Techniques Used:

    Results of the base CNN model: (a) Training and validation loss and (b) training and validation accuracy.
    Figure Legend Snippet: Results of the base CNN model: (a) Training and validation loss and (b) training and validation accuracy.

    Techniques Used: Biomarker Discovery

    Development of performance metrics for the base CNN model.
    Figure Legend Snippet: Development of performance metrics for the base CNN model.

    Techniques Used:

    Confusion matrix for the base CNN model.
    Figure Legend Snippet: Confusion matrix for the base CNN model.

    Techniques Used:

    ROC curve for the base CNN model.
    Figure Legend Snippet: ROC curve for the base CNN model.

    Techniques Used:



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    Image Search Results


    Base CNN model architecture.

    Journal: Food Science & Nutrition

    Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

    doi: 10.1002/fsn3.70513

    Figure Lengend Snippet: Base CNN model architecture.

    Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

    Techniques:

    Results of the base CNN model: (a) Training and validation loss and (b) training and validation accuracy.

    Journal: Food Science & Nutrition

    Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

    doi: 10.1002/fsn3.70513

    Figure Lengend Snippet: Results of the base CNN model: (a) Training and validation loss and (b) training and validation accuracy.

    Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

    Techniques: Biomarker Discovery

    Development of performance metrics for the base CNN model.

    Journal: Food Science & Nutrition

    Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

    doi: 10.1002/fsn3.70513

    Figure Lengend Snippet: Development of performance metrics for the base CNN model.

    Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

    Techniques:

    Confusion matrix for the base CNN model.

    Journal: Food Science & Nutrition

    Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

    doi: 10.1002/fsn3.70513

    Figure Lengend Snippet: Confusion matrix for the base CNN model.

    Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

    Techniques:

    ROC curve for the base CNN model.

    Journal: Food Science & Nutrition

    Article Title: Enhanced Maize Leaf Disease Detection and Classification Using an Integrated CNN ‐ ViT Model

    doi: 10.1002/fsn3.70513

    Figure Lengend Snippet: ROC curve for the base CNN model.

    Article Snippet: Base CNN , Kaggle + Mendeley , Fold 1 , 97.85 , 97.80 , 97.76 , 97.78.

    Techniques: