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Arsenazo-DBN

Known as: 3-((2-arsonophenyl)azo)-6-((2,6-dibromo-4-nitrophenyl)azo)-4,5-dihydroxy-2,7-naphthalenedisulfonic acid 
National Institutes of Health

Related topics

Related topics

2 relations

Broader (1)

Arsenazo III
analogs & derivatives

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Deep Belief Network Modeling for Automatic Liver Segmentation
The liver segmentation in CT scan images is a significant step toward the development of a quantitative biomarker for computer… 
2019
2019
Prediction of electricity consumption in cement production: a time-varying delay deep belief network prediction method
An important energy consumption index in cement production process is electricity consumption whose accurate prediction is of… 
2018
2018
Statistical monitoring of a wastewater treatment plant: A case study.
Highly Cited
2017
Highly Cited
2017
A deep belief network with PLSR for nonlinear system modeling
2017
2017
Deep learning cognitive radar for micro UAS detection and classification
In this paper, we propose an intelligent cognitive radar system for detecting and classifying the micro unmanned aerial systems… 
2017
2017
Investigation of activation functions in deep belief network
Deep Belief Network (DBN) is made up of stacked Restricted Boltzmann Machine layers associated with global weight fine-tuning for… 
Review
2016
Review
2016
A brief survey on deep belief networks and introducing a new object oriented toolbox ( DeeBNet V 3 . 0 )
Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep… 
2012
2012
Deep Attribute Networks
Obtaining compact and discriminative features is one of the major challenges in many of the real-world image classification tasks… 
Highly Cited
2010
Highly Cited
2010
Convolutional Deep Belief Networks on CIFAR-10
We describe how to train a two-layer convolutional Deep Belief Network (DBN) on the 1.6 million tiny images dataset. When… 
Highly Cited
2007
Highly Cited
2007
Real‐time Bayesian anomaly detection in streaming environmental data
With large volumes of data arriving in near real time from environmental sensors, there is a need for automated detection of… 
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