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We provide evidence that the algorithm is second-order accurate on various exact solutions and compare the adaptive and nonadaptive calculations.
This scenario is supported by the time responses reported later in the document that provides evidence that the algorithm could be executed against a large number of time series within a very short period of time.
The quality of practical results produced by the method and its programming implementation provide evidence that the algorithm can be applied to other finite element models with various physical backgrounds.
This analysis, however, provides very little evidence that the algorithm is actually capable of quality predictions.
The simulation results also provide evidence that the algorithm is at least able to find a small number of true/known clusters when they exist.
The PCR results for the 6 selected splice variants give strong evidence that the algorithm used to predict potential alternative splice forms is very reliable.
Similar(52)
Numerical evidence shows that the algorithm converges very fast, usually in a few minutes whereas other algorithms, such as the Bayesian method, seem to be computationally expensive and also slow to converge.
The performance of the classifier algorithm on each population and corresponding 3-year DMFS is shown in Table 1, providing supporting evidence that the classifier algorithm is not population dependent.
The results gave evidence that the proposed algorithm is the best option for higher strengths.
Also, computational study provides evidence that the genetic algorithm delivers reasonably good solutions while requiring significantly shorter CPU times to solve this problem.
This result provides strong evidence that the delta algorithm model and ARIMA model results are not from the same population (i.e., the difference between modeling results is real).
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CEO of Professional Science Editing for Scientists @ prosciediting.com