scholarly journals Modulation Format Recognition Using Artificial Neural Networks for the Next Generation Optical Networks

2018 ◽  
Author(s):  
Latifa Guesmi ◽  
Habib Fathallah ◽  
Mourad Menif
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Networks ◽  
Next Generation ◽  
Modulation Format ◽  
Artificial Neural

scholarly journals An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

2019 ◽  
Author(s):  
René Janßen ◽  
Jakob Zabel ◽  
Uwe von Lukas ◽  
Matthias Labrenz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Environmental Monitoring ◽  
Microbial Communities ◽  
List Type ◽  
Neural Net ◽  
Next Generation ◽  
Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

On the predictability of next generation mobile network traffic using artificial neural networks

2013 ◽  
Vol 28 (8) ◽  
pp. 1484-1492 ◽  
Author(s):  
I. Loumiotis ◽  
E. Adamopoulou ◽  
K. Demestichas ◽  
T. Stamatiadi ◽  
M. Theologou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Traffic ◽  
Mobile Network ◽  
Next Generation ◽  
Artificial Neural

scholarly journals DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

2017 ◽  
Vol 28 ◽  
pp. 225-236 ◽  
Author(s):  
Athina Vidaki ◽  
David Ballard ◽  
Anastasia Aliferi ◽  
Thomas H. Miller ◽  
Leon P. Barron ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dna Methylation ◽  
Artificial Neural Networks ◽  
Next Generation Sequencing ◽  
Next Generation ◽  
Artificial Neural ◽  
Generation Sequencing ◽  
Age Prediction

scholarly journals Model transfer of QoT prediction in optical networks based on artificial neural networks

2019 ◽  
Vol 11 (10) ◽  
pp. C48 ◽  
Author(s):  
Jiakai Yu ◽  
Weiyang Mo ◽  
Yue-Kai Huang ◽  
Ezra Ip ◽  
Daniel C. Kilper
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Networks ◽  
Model Transfer ◽  
Artificial Neural

scholarly journals Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks

2012 ◽  
Vol 20 (11) ◽  
pp. 12422 ◽  
Author(s):  
Faisal Nadeem Khan ◽  
Yudi Zhou ◽  
Alan Pak Tao Lau ◽  
Chao Lu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fiber Optic ◽  
Modulation Format ◽  
Artificial Neural ◽  
Fiber Optic Networks
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