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Titlebook: Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications; 9th International Wo José Manuel Ferrández Vicente,Jos

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發(fā)表于 2025-3-28 18:09:20 | 只看該作者
Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications9th International Wo
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發(fā)表于 2025-3-28 22:24:21 | 只看該作者
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發(fā)表于 2025-3-29 01:55:45 | 只看該作者
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發(fā)表于 2025-3-29 03:45:20 | 只看該作者
https://doi.org/10.1007/978-3-476-04175-3nto a Convolutional Neural Network (CNN), which is used to identify the informative patterns within the CDT drawings that are relevant for the assessment of the patient’s cognitive status. Performance is evaluated in a real context where differentiating between CI patients and controls. The proposed
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發(fā)表于 2025-3-29 07:38:46 | 只看該作者
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發(fā)表于 2025-3-29 15:12:44 | 只看該作者
Sylvia Feil,J?rg Resag,Kristin Riebert transform. Subsequently, the cause-effect relationships between channels in each subject have been reflected thanks to Granger causality, obtaining matrices that reflect the interaction between the different parts of the brain. Hence, each subject was classified as belonging or not to the control
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發(fā)表于 2025-3-29 19:06:38 | 只看該作者
Chemie der Erde und der Planeten, (SIT2FLS) methods. In this step, the DL features and the each functional connectivity features are combined using a concatenate layer and finally classified by the sigmoid activation layer. To better evaluate the results, K-Fold with . was used in the classification step. The results show that the
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發(fā)表于 2025-3-29 21:57:50 | 只看該作者
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發(fā)表于 2025-3-30 03:30:37 | 只看該作者
Benjamin Bahr,J?rg Resag,Kristin Riebebeen assessed with images from the Alzheimer Disease Neuroimaging Initiative, demonstrating that using the new mixed mode image outperforms the classification accuracy obtained by individual MRI or PET images.
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發(fā)表于 2025-3-30 04:27:52 | 只看該作者
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