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The evaluated methods are clustered considering the main steps of problem solving.
The present study evaluated methods reporting and methodological choices across 241 recent fMRI articles.
Among the evaluated methods, direct ELISA exhibited the highest accuracy in toxin detection.
The results indicate that the proposed method achieved the best performance among the evaluated methods, with overall accuracy 3 5% higher than the existing best-performing method.
We also evaluated methods of mandibular reconstruction featuring virtual planning, the use of surgical guides, and laser printing of custom titanium bony plates to support composite free flaps, and evaluated their utility.
We developed and evaluated methods for the analysis and interpretation of the baseline risk heterogeneity in meta-analysis of individual patient data (MIPD) based on information on predictive factors.
On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume.
The results show that the proposed method performs best among the evaluated methods.
SW1PerS is shown to be the most shape-agnostic of the evaluated methods, and the only one to consis- tently classify damped signals as highly periodic.
SW1PerS is shown to be the most shape-agnostic of the evaluated methods, and the only one to consistently classify damped signals as highly periodic.
The evaluated methods included both classical feature-based methods and the state-of-the-art deep learning approach.
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