Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep Apnea


Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep Apnea – We present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.

In this paper, we propose a new deep neural-image visual learning approach called Deep-Named Entity Recognition, which is designed for text text and for image text. The proposed method includes a novel deep neural network architecture that is capable of both recognition and identification tasks. This architecture is designed as a representation of a text text, where words are arranged in a tree, and each node has corresponding information about the tree and about the text. This structure has been extensively explored so far, using both supervised and unsupervised training. The architecture, which is designed to exploit both text and images, is fully automated and fully distributed, making it possible to test the proposed model on a large corpus of text texts. The proposed architecture is tested on both text and image-text datasets. The results show that the proposed deep networks outperform state-of-the-art deep architectures.

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Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep Apnea

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  • Multilayer perceptron with segmentated train

    User-driven indexing of papers in Educational Data MiningIn this paper, we propose a new deep neural-image visual learning approach called Deep-Named Entity Recognition, which is designed for text text and for image text. The proposed method includes a novel deep neural network architecture that is capable of both recognition and identification tasks. This architecture is designed as a representation of a text text, where words are arranged in a tree, and each node has corresponding information about the tree and about the text. This structure has been extensively explored so far, using both supervised and unsupervised training. The architecture, which is designed to exploit both text and images, is fully automated and fully distributed, making it possible to test the proposed model on a large corpus of text texts. The proposed architecture is tested on both text and image-text datasets. The results show that the proposed deep networks outperform state-of-the-art deep architectures.


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