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We show the applicability of our approach using several design and intensification case studies.
We extensively evaluate our approach using several challenging benchmarks, and provide comparative results to related state-of-the-art techniques.
We carried out an extensive evaluation of the approach using several open-source enterprise applications, and a closed-source system from our industrial project partner Science Warehouse [9].
Indicators selection is used to improve diagnosis performances by the help of a hybrid approach using several selection criteria and different classifiers.
It is only in recent years that research has turned towards a sensor fusion approach using several different technologies in single systems or devices.
We test the capabilities of our approach using several examples, in which we compare it against other well-known clustering methods.
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The approach uses several algorithms to support the detection of the crosscutting constraints in meta-model-based model transformations.
This approach uses several arbitrary thresholds (e.g. 300 °C for the maximum temperature composite image, Fig. 2c) based on rough trial and error, and these thresholds could be improved with a more rigorous optimization.
The approach uses several methods to evaluate functional similarities, and putative coregulation patterns to define the similarities and differences between expression dynamics across organisms and between in vitro and in vivo models of influenza infection.
We believe that a combination of phylogenetic and map-based approaches using several species is much more useful to resolve the evolutionary history of gene families as has been pointed out earlier [ 50, 51].
Computational results are presented for the proposed solution approaches using several real as well as simulated realistic test problems pertaining to high schools in Kuwait.
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