scholarly journals Obstructive sleep apnoea detection using convolutional neural network based deep learning framework

2017 ◽  
Vol 8 (1) ◽  
pp. 95-100 ◽  
Author(s):  
Debangshu Dey ◽  
Sayanti Chaudhuri ◽  
Sugata Munshi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
Learning Framework ◽  
Obstructive Sleep ◽  
Apnoea Detection

scholarly journals Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129889-129898
Author(s):  
Xin Dong ◽  
Yizhao Zhou ◽  
Lantian Wang ◽  
Jingfeng Peng ◽  
Yanbo Lou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Liver Cancer ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Learning Framework

scholarly journals A structure-based deep learning framework for protein engineering

2019 ◽  
Author(s):  
Raghav Shroff ◽  
Austin W. Cole ◽  
Barrett R. Morrow ◽  
Daniel J. Diaz ◽  
Isaac Donnell ◽  
...  
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Deep Learning ◽  
Protein Engineering ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
A Priori ◽  
Novel Proteins ◽  
Learning Framework

AbstractWhile deep learning methods exist to guide protein optimization, examples of novel proteins generated with these techniques require a priori mutational data. Here we report a 3D convolutional neural network that associates amino acids with neighboring chemical microenvironments at state-of-the-art accuracy. This algorithm enables identification of novel gain-of-function mutations, and subsequent experiments confirm substantive phenotypic improvements in stability-associated phenotypes in vivo across three diverse proteins.

scholarly journals Application of screening test for obstructive sleep apnoea detection in systemic hypertensive patients

2019 ◽  
Vol 9 (1) ◽  
pp. 11-14
Author(s):  
Anil Sontakke ◽  
◽  
Dikshant R Kawale ◽  
Jaydeep Nayse ◽  
Sonal Arsude ◽  
...  
Keyword(s):  
Obstructive Sleep Apnoea ◽  
Screening Test ◽  
Sleep Apnoea ◽  
Hypertensive Patients ◽  
Obstructive Sleep ◽  
Apnoea Detection

Deep Multi-Instance Multi-Label Learning for Image Annotation

2017 ◽  
Vol 32 (03) ◽  
pp. 1859005 ◽  
Author(s):  
Hai-Feng Guo ◽  
Lixin Han ◽  
Shoubao Su ◽  
Zhou-Bao Sun
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real World ◽  
Supervised Classification ◽  
Image Annotation ◽  
Learning Framework ◽  
Recent Advancement ◽  
Real World Datasets

Multi-Instance Multi-Label learning (MIML) is a popular framework for supervised classification where an example is described by multiple instances and associated with multiple labels. Previous MIML approaches have focused on predicting labels for instances. The idea of tackling the problem is to identify its equivalence in the traditional supervised learning framework. Motivated by the recent advancement in deep learning, in this paper, we still consider the problem of predicting labels and attempt to model deep learning in MIML learning framework. The proposed approach enables us to train deep convolutional neural network with images from social networks where images are well labeled, even labeled with several labels or uncorrelated labels. Experiments on real-world datasets demonstrate the effectiveness of our proposed approach.

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