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Table 1 Big data compression methods References Methods Description Strengths Weaknesses Yang et al. [45] Spatiotemporal The proposed method performs online clustering of streaming data by correlating similarities in their time series to share workload in the clusters.
In references, methods in [4 7] suggest to mitigate the interference that cellular users suffer by either limiting the D2D user's transmit power or choosing the D2D users only in the interference limited area.
The differences in sensitivity and specificity rates may be explained by the reference population, adopted cut-offs from various references, methods used to assess %BF, and the cut-offs for excess fat [ 31, 33].
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That is, the proposed method outperforms the reference methods.
No-reference methods rely only on the degraded video.
Herein, we discuss its advantages to the reference methods.
Significant improvement over both reference methods can be seen.
One example is the implementation of multi-reference methods.
Both reference methods utilised filtration of water samples.
Our experiments include a comparison to common reference methods.
The DD method [5] is applied to all these reference methods and the BDSAE method.
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