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    Self-Supervised Learning with Adaptive Frequency-Time Attention Transformer for Seizure Prediction and Classification by Yajin Huang, Yuncan Chen, Shimin Xu, Dongyan Wu, Xunyi Wu

    Published 2025-04-01
    “…Background: In deep learning-based epilepsy prediction and classification, enhancing the extraction of electroencephalogram (EEG) features is crucial for improving model accuracy. …”
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    Prediction of Key Quality Parameters in Hot Air-Dried Jujubes Based on Hyperspectral Imaging by Quancheng Liu, Chunzhan Yu, Yuxuan Ma, Hongwei Zhang, Lei Yan, Shuxiang Fan

    Published 2025-05-01
    “…The results demonstrate that the SG1st preprocessing method significantly enhanced the predictive capability of the model. For the predictions of <i>L</i>*, <i>a</i>*, <i>b</i>*, SSC, hardness, and moisture content, the best inversion models achieved coefficients of determination <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula> of 0.9972, 0.9970, 0.9857, and 0.9972, respectively. …”
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