Titlebook: Engineering Applications of Neural Networks; 16th International C Lazaros Iliadis,Chrisina Jayne Conference proceedings 2015 Springer Inter

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书目名称Engineering Applications of Neural Networks影响因子(影响力)




书目名称Engineering Applications of Neural Networks影响因子(影响力)学科排名




书目名称Engineering Applications of Neural Networks网络公开度




书目名称Engineering Applications of Neural Networks网络公开度学科排名




书目名称Engineering Applications of Neural Networks被引频次




书目名称Engineering Applications of Neural Networks被引频次学科排名




书目名称Engineering Applications of Neural Networks年度引用




书目名称Engineering Applications of Neural Networks年度引用学科排名




书目名称Engineering Applications of Neural Networks读者反馈




书目名称Engineering Applications of Neural Networks读者反馈学科排名

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Can We Be ‘Good’ at Interculturality?te various expressiveness. The results of the study show that for a string node using distinct temperatures distinct phase transitions are shown. As result of phase transitions, changed centroids of respective groupings are observed. At each node, different behaviors revealed by carbon sources applied to genes are observed.

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galley

Teacher Education Policy and Researched and implemented a time-efficient distributed modular application for sequence alignment, phylogenetic profiling and clustering of protein sequences, by utilizing the European Grid Infrastructure. The optimal utilization of the Grid with regards to the respective modules, allowed us to achieve significant speedups to the order of 1400%.

galley

https://doi.org/10.1007/978-1-349-09003-7he Logitboost regression tree model is more confident at the unlabeled instances. We performed a comparison with other well-known semi-supervised classification methods on standard benchmark datasets and the presented technique had better accuracy in most cases.

替代品

Conference proceedings 2015n Rhodes, Greece, in September 2015. .The 36 revised full papers presented together with the abstracts of three invited talks and two tutorials were carefully reviewed and selected from 84 submissions. The papers are organized in topical sections on industrial-engineering applications of ANN; bioinf

MOTTO

Sally Knipe,Rebecca Miles,Stephanie Garoni in this work the prediction of the surface roughness (..) evolution based on Recurrent Neural Networks is presented with the capability to generalize to new grinding wheels and conditions. Results show excellent prediction of the surface finish evolution. The absolute maximum error is below 0.49µm, being the average error around 0.32µm.

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