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1d-cnn and bi-lstm models  (StatLog Inc)

 
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    StatLog Inc 1d-cnn and bi-lstm models
    1d Cnn And Bi Lstm Models, supplied by StatLog 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/1d-cnn+model/1d+cnn+and+bi+lstm+models/pm36552906-479-0-50
    Average 90 stars, based on 1 article reviews
    1d-cnn and bi-lstm models - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Activation Assay:

    Article Title: A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models
    Article Snippet: In b, we can see that the 1D-CNN model successfully detected 9 of 10 HD patients and 13 of 17 healthy persons when applied to the Statlog dataset.

    Article Title: A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models.
    Article Snippet: In Figure 10b, we can see that the 1D-CNN model successfully detected 9 of 10 HD patients and 13 of 17 healthy persons when applied to the Statlog dataset.



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    Image Search Results


    The 1D CNN architecture for the pretext task.

    Journal: Applied sciences (Basel, Switzerland)

    Article Title: Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

    doi: 10.3390/app132112035

    Figure Lengend Snippet: The 1D CNN architecture for the pretext task.

    Article Snippet: They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [ ].

    Techniques:

    The 1D CNN architecture for fine-tuning the pre-trained network towards the downstream stress-prediction task.

    Journal: Applied sciences (Basel, Switzerland)

    Article Title: Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

    doi: 10.3390/app132112035

    Figure Lengend Snippet: The 1D CNN architecture for fine-tuning the pre-trained network towards the downstream stress-prediction task.

    Article Snippet: They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [ ].

    Techniques:

    Number of days in the sample, MASH model used, and sleep–wake interval size

    Journal: Journal of activity, sedentary and sleep behaviors

    Article Title: Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep–wake cycles

    doi: 10.1186/s44167-023-00017-5

    Figure Lengend Snippet: Number of days in the sample, MASH model used, and sleep–wake interval size

    Article Snippet: The AUC-ROC for both 1D CNN models developed for MASH (ActiGraph + Actiwatch or ActiGraph-only) were considered excellent ( ) with values of 0.991 and 0.983 for the ActiGraph + Actiwatch and ActiGraph-only models, respectively.

    Techniques: