Sentence examples for approaches for binary from inspiring English sources

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Figure 2 Performance comparison of fusion and diversity approaches for binary consensus over regular ring lattice topology.

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Motivated by this discovery, we propose a layered Onion Approach for Binary Authorship Attribution called OBA2.

A multi-fluid computational fluid dynamics (CFD) model based on kinetic theory of granular flow and Eulerian Eulerian approach for binary mixture of particles was presented.

We propose a hybrid evolutionary neural approach for binary classification that incorporates a special training data over-fitting minimizing selection procedure for improving the prediction accuracy on holdout sample.

A matrix of 22 mixtures was developed using this statistical approach for binary fuel blends of 100% methane to 100% ethane; pressure ranges of 1, 11 16, and 25 31 atm; equivalence ratios of 0.5, 1.0, and 2.0; over a temperature range of 1154 2248 K, and argon dilutions of 98%, 95 %, 85, and 75%.

Therefore, the fusion approach for binary consensus over not fully connected and time-invariant network topology and in the presence of noise and fading suffers from asymptotic memoryless behavior.

Fig. 6 Memory removal via decision feedback equalization: a symbol error probability of MFT-ESA approach for binary CPFSK with zero-forcing and MMSE decision feedback equalization to remove memory induced due to partial response signaling and b symbol error probability for multilevel CPFSK with zero-forcing and MMSE decision feedback equalization.

Since the outcome of our study is binary (HCV positive or negative), we describe the approach for binary outcomes.

For all other outcomes we used the random effects approach for binary data of DerSimonian and Laird, where τ2 was estimated with the inverse variance method.

In our previous work (Kärkkäinen and Sillanpää 2012a) we already have considered a kindred threshold approach for binary traits in MAP-estimation context.

It seems unlikely that this is solely the result of limitations associated with our analytical approach; while the predictive power of a binary response model may be low, phylogenetic regression constitutes a robust analytical approach for binary response variables [ 75].

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