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The Antoine method gave the worst predictions, with deviations as high a 293%, whilst the other two methods gave varying but still significant deviations, indicating differing sensitivity of the methods to different parts of the process.
Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed.
The chapter then discusses the details of applying such test methods to different home textile merchandises.
Fig. 5 Robustness of machine learning methods to different levels of noise for 4 out of 7 activity classes.
Future work will focus on the extensions of the proposed methods to different data factorization approaches such as the ICA.
To fully investigate the sensitivity of these methods to different parameters, several concrete slabs were tested with the Portable Seismic Property Analyzer (PSPA).
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This demonstrates the robustness of our method to different initializations.
Eight beams are designed and subjected to accelerated method to different corrosion levels.
To investigate the convergence of the methods, we apply our method to different values of fractional orders.
Extending the method to different imaging modalities is a direction for further research.
We applied the RGPFinder method to different bacterial genome sets.
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