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1d cnn model  (Kuang Lung Shing)

 
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    Kuang Lung Shing 1d cnn model
    1d Cnn Model, supplied by Kuang Lung Shing, 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+model/10__1016_slash_j__jfca__2024__106655-213-11-13
    Average 90 stars, based on 1 article reviews
    1d cnn model - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: Optimizing protein content prediction in rice bean (Vigna umbellata L.) by integrating near-infrared reflectance spectroscopy, MPLS, deep learning, and key wavelengths selection algorithms
    Article Snippet: Rice bean, a potential protein-rich underutilized crop, can serve as a valuable source for food and feed, contributing to sustainable food and nutrition security.. However, traditional methods for measuring protein content are laborious, tedious, and expensive.. This study evaluates the performance of Modified Partial Least Squares (MPLS) and 1D Convolutional Neural Network (1D CNN) models in predicting protein content in rice bean using Near-Infrared Reflectance Spectroscopy (NIRS) data.



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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: