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    Solar Flare Forecasting Using Hybrid Neural Networks by Dan Xu, Pengchao Sun, Song Feng, Bo Liang, Wei Dai

    Published 2025-01-01
    “…Therefore, it is crucial to establish highly accurate solar flare prediction models to enable humans to anticipate solar flare eruptions in advance, thereby reducing human and economic losses. In this paper, we utilized the solar active region (AR) magnetogram provided by the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager and the associated feature parameters of the magnetic field; specifically, the feature vectors of the magnetic field’s spatial structure characteristics and the magnetic field feature parameters are fused to predict solar flares. …”
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    Predicting breast cancer recurrence using deep learning by Deepa Kumari, Mutyala Venkata Sai Subhash Naidu, Subhrakanta Panda, Jabez Christopher

    Published 2025-01-01
    “…This study introduces the BCR-HDL (Breast Cancer Recurrence using Hybrid Deep Learning) framework, a novel approach designed to predict breast cancer recurrence with high accuracy and interpretability. Utilizing the Wisconsin Diagnostic Breast Cancer and Wisconsin Prognostic Breast Cancer datasets, the framework integrates multiple deep learning architectures- Multi layer Perceptron (MLP), Visual Geometry Group (VGG), Residual Network (ResNet), and Extreme Inception (Xception)-with traditional machine learning models such as Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR). …”
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