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Titlebook: Brain Informatics; 15th International C Mufti Mahmud,Jing He,Ning Zhong Conference proceedings 2022 Springer Nature Switzerland AG 2022 art

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楼主: dilate
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Enhancing the MR Neuroimaging by Using the Deep Super-Resolution Reconstructionation. In particular, the technological development and innovation of the Magnetic Resonance Imaging (MRI) machine, through increasing the magnetic field from low (such as 3T) to high (such as 7T), has revealed significant advantages regarding the image quality enhancement, etc. Currently, due to th
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https://doi.org/10.1007/978-3-642-18747-6 the low dimensional subspace to employ principal components using PCA and finally GRNN (General Regression Neural Network) is presented for effective classification of four different emotional states from publicly available EEG based GAMEEMO dataset. The experimental results are promising and performed well, compared to other state of methods.
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Anterior Cruciate Ligament (ACL),ptimally set parameters of information theoretic analysis of astrocytic activities. Applications of our techniques to simulated and real astrocytic data show how to set parameters of information analyses that provide conservative, yet reliable, estimates of astrocytic calcium dynamics contribution to circuit-level brain information processing.
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Imaging of Malignant Focal Liver Lesions,s modest. The latent representations extracted by the autoencoders can then be given as input to regularized regression methods to predict neurophsychological scores, significantly outperforming recently proposed methods based on linear feature extraction.
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COSLETS: Recognition of Emotions Based on EEG Signals the low dimensional subspace to employ principal components using PCA and finally GRNN (General Regression Neural Network) is presented for effective classification of four different emotional states from publicly available EEG based GAMEEMO dataset. The experimental results are promising and performed well, compared to other state of methods.
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Intracranial Space-Occupying Lesionso TD (.). The automated classification with an accuracy of 96.2% supported the significance of the metrics in distinguishing ASD from TDs. The present findings revealed that social conformity and social influence imparted growth in ASD cognition.
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https://doi.org/10.1007/978-3-642-76790-6ferences (such as edges with large weight or centrality differences). We have also applied the proposed method in the comparison between a Major Depressive Disorder (MDD) group versus healthy controls and briefly report the resulting set of connections that cause most of the observed cascade differences.
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