Titlebook: Exploring Service Science; 10th International C Henriqueta Nóvoa,Monica Drăgoicea,Niklas Kühl Conference proceedings 2020 The Editor(s) (if

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书目名称Exploring Service Science影响因子(影响力)




书目名称Exploring Service Science影响因子(影响力)学科排名




书目名称Exploring Service Science网络公开度




书目名称Exploring Service Science网络公开度学科排名




书目名称Exploring Service Science被引频次




书目名称Exploring Service Science被引频次学科排名




书目名称Exploring Service Science年度引用




书目名称Exploring Service Science年度引用学科排名




书目名称Exploring Service Science读者反馈




书目名称Exploring Service Science读者反馈学科排名

Interregnum

决定性

Trinidad de Miguel,Oude Zhu,Tomás G. Villa an Italian airline by applying a Principal Component Analysis (PCA) - Data Envelopment Analysis (DEA) model and we verify which airline routes are ranked among the most efficient ones by also including, in the proposed model, the presence of this undesirable output.

倒转

Nepal: First Steps Towards Redressing HIs?, (k-NN) and the Singular Value Decomposition (SVD), with Feed-Forward Neural Networks; given these assumptions, we finally demonstrated that a “Deep” Neural architecture could achieve better results in terms of “loss” generated by the model, laying the foundations for a new, innovative paradigm in service recommendation science.

禁令

https://doi.org/10.1007/978-3-8350-9274-7n RVM. We contribute by developing and evaluating an approach for hourly forecasts in a retail setting – this combination of application domain and forecast granularity is novel. A trace-driven simulation confirms that the forecasting-based approach leads to less downtime and costs than naïve emptying strategies.

真

真

Thyroiditis

artifice

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