Classification of Brain Areas Using Convolutional Neural Networks


Classification of Brain Areas Using Convolutional Neural Networks – In this paper, we propose an end-to-end method for predicting Alzheimer’s Disease (AD) in brain tissue by using unsupervised learning. In this paper, an attention based classifier (AD-C) is proposed for Alzheimer’s Disease (DAD) prediction, based on a deep feature-based model which can learn the visual features of the brain regions that are related with AD. Moreover, an Alzheimer’s DAD prediction model is trained by using the spatial domain feature representation based on the spatial relationship between features. Moreover, a deep feature-based classifier is used as the model by using a recurrent network in the deep data and a recurrent neural network. Experiments on two different AD datasets have been performed to evaluate the performance of the proposed approach. The obtained results demonstrate that the proposed AD-C model can improve the performance of the proposed AD-C model prediction method.

We present an extension to the Conditional Independence Process (CI) that allows us to make predictions about the distribution of a latent variable given a posterior. The CI is a generalization of the classical CI, and therefore a nonparametric probabilistic estimator for the conditional independence. We study these probabilistic estimators on the CINF and CIFAR-10 datasets. By training the CI, we learn more about the conditional independence between latent variables, allowing for faster inference in terms of the latent parameters. We also propose an efficient inference algorithm based on Bayesian networks for this problem. Using the CI, we also design a new inference algorithm which approximates the Cinformatrix function in an empirical manner. We validate the proposed method on data sets with high latent variables in order to verify its potential.

An Iterative Envelope-Train Ensemble to Characterize and Classifiers Fusion

Annotating Temporal Memory Policies

Classification of Brain Areas Using Convolutional Neural Networks

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  • A Novel Bayes-Optimal Bayesian Network Classifier for Non-Gaussian Event Detection

    The Dynamics of Hidden Variables in Conditional Independence DistributionsWe present an extension to the Conditional Independence Process (CI) that allows us to make predictions about the distribution of a latent variable given a posterior. The CI is a generalization of the classical CI, and therefore a nonparametric probabilistic estimator for the conditional independence. We study these probabilistic estimators on the CINF and CIFAR-10 datasets. By training the CI, we learn more about the conditional independence between latent variables, allowing for faster inference in terms of the latent parameters. We also propose an efficient inference algorithm based on Bayesian networks for this problem. Using the CI, we also design a new inference algorithm which approximates the Cinformatrix function in an empirical manner. We validate the proposed method on data sets with high latent variables in order to verify its potential.


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