Titlebook: Komplikationen in der Neurologie; Frank Block Book 2016 Springer-Verlag Berlin Heidelberg 2016 Behandlungsfehler.Fehlermanagement.Komplika

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書目名稱Komplikationen in der Neurologie影響因子(影響力)




書目名稱Komplikationen in der Neurologie影響因子(影響力)學(xué)科排名




書目名稱Komplikationen in der Neurologie網(wǎng)絡(luò)公開度




書目名稱Komplikationen in der Neurologie網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Komplikationen in der Neurologie被引頻次




書目名稱Komplikationen in der Neurologie被引頻次學(xué)科排名




書目名稱Komplikationen in der Neurologie年度引用




書目名稱Komplikationen in der Neurologie年度引用學(xué)科排名




書目名稱Komplikationen in der Neurologie讀者反饋




書目名稱Komplikationen in der Neurologie讀者反饋學(xué)科排名

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S. Preu?eindings revealed a set of twenty-one recommendations to take into account when implementing cloud computing in the government sector, such as using a pilot project, training, and consulting, among others. These recommendations are useful to guide the IT decision makers in the process of cloud comput

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J. P. Sieb demonstrate the ability of the proposed technique to generate an adaptive resource allocation in response to the variation on application data size and network bandwidth. Results reveal that the proposed technique improves the execution time for data-intensive applications by an average of 78% and

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F. Blockter simulated by Xen. Our evaluation results show that Chameleon can flag the hotspots accurately and timely. Furthermore, the policy of resources estimation for the VMs helps Chameleon to make the decision of selecting the under-load PM, in order to mitigate the risk of secondary hotspot.

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F. Blockter simulated by Xen. Our evaluation results show that Chameleon can flag the hotspots accurately and timely. Furthermore, the policy of resources estimation for the VMs helps Chameleon to make the decision of selecting the under-load PM, in order to mitigate the risk of secondary hotspot.

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F. Blockconstruction error of the stacked autoencoder on the current window is employed. The Page-Hinckley test dynamically calculates thresholds to warn and alarm concept drift. Experimental results indicate that DD-SAPH outperforms the compared unsupervised algorithms when addressing concept drift on both