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Automatic methods have been developed for assessing credibility of Twitter-based data sets [6].
Recently, automatic methods have been proposed to deal with this kind of data (Antonov et al., 2009; Dittrich et al., 2008; Faust et al., 2010; Leader et al., 2011).
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Manual and semi-automatic methods have been proposed to perform the registration stage in face animation.
In previous work, an automatic method has been developed in order to compute 3D models of real world scenes from laser range data.
To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina.
In addition, the improved automatic method has a good reproducibility and repeatability; the corresponding design program can be hammered repeatedly to duplicate the same patterns for many times, and the pattern size can also be changed easily through adjusting the program parameters.
A semi-automatic method has also been proposed based the topological difference between real and random co-expression networks [ 8].
To overcome this problem, several automatic delineation methods have been proposed [18 23], but up to the present, no general consensus about the best method exists.
Several automatic clustering methods have already been proposed (e.g., Frohlich and Davis 1990; Ogata and Katsura 2014), which are suitable for the analysis of big datasets.
Several automatic, noninvasive methods have been developed to detect patient-ventilator asynchrony.
To overcome these limitations, semi-automatic alignment methods have been proposed where expert knowledge can be used to guide the alignment procedure.
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