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Obviously, high-precision recognition of introns is required for efficient splicing, and this recognition depends on an hierarchy of signals of varying specificity that are located both in the intron and in the exon and interact with different parts of the spliceosomal complex.
As shown in Fig. 5 b, the performance improved by 3 6%%, compared to the results in Fig. 5 a. Fig. 5 Evaluation of face registration: the red line is the recognition precision on CK+ dataset; the green line is the recognition precision on MMI dataset; the blue line is the recognition precision on AFEW dataset.
b Recognition precision introduced face registration into training stages.
The general recognition precision of the SVC is more than 85% for unspecific versatile malware.
In addition, Table 1 shows the recognition precision of a main object used in the experiment.
The maximum recognition precision of the proposed method was 86.3%, and the worst result was 69.3%.
a Recognition precision introduced face registration into both the training and recognizing stages.
Recognition precision by keystroke dynamics may be affected in the presence of keyboards with different characteristics in the same environment.
Jia, et al. [19], improved the shape recognition precision significantly through combining the genetic algorithm, fuzzy theory, and neural network.
Table 3 Recognition precision on AR-08 and AR-21 (a), AR-11 and AR-24 (b) Open image in new window.
Zhang, et al. [15], combined the genetic algorithm with the back propagation neural network to improve the capability and precision of shape recognition.
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