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We conclude that our overruling AND-combination of perceptron confidence and resetting counter mechanism outperforms the previously proposed confidence scheme.
The proposed confidence check (Figure 2c, [22]) can remove most of these noisy disparities while the rest can be filtered by speckle removal (Figure 2d, [2]).
Coverage properties of the proposed confidence intervals are studied by Monte Carlo simulation, and are found to be excellent in a variety of circumstances.
Using these data, the various reliability parameters, which affects the system performance, are obtained in the form of the fuzzy membership function by the proposed confidence interval based fuzzy Lambda-Tau (CIBFLT) methodology.
In order to deal with transcription errors and OOV words, some modifications of the traditional vector space model (VSM) indexing framework [21] are proposed: confidence measures are taken into account in the index term weights, and a phonetic-based document retrieval technique, enabling to retrieve in a transcript the proper nouns contained in a synopsis, is also considered.
To further demonstrate the usefulness of the proposed confidence interval, we applied it to three previously published data sets [6], [9], [10].
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On the minus side, Jalili and his team barely glanced at the P5+1 proposed confidence-building deal that was handed to them at the beginning of the talks, and did not refer to it directly for the rest of the Baghdad meeting.
We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in.
The proposed confidence-based Pareto dominance allows us to design different confidence-based robust optimisation variants of meta-heuristics based on different methods.
The simulation results show that the coverage percentages of the proposed generalized confidence intervals are quite close to the nominal coverage probabilities, even for small sample sizes.
The results are presented in figures 1, 2, 3. Analysis of figure 1 indicates that the proposed 95% confidence interval for W approximates the desired coverage of 95% when W is between 0.5 and 1.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com