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More and more researchers are realizing that the system performance of VANETs can be improved by utilizing traffic conditions.
First, VST and MST models can be improved by utilizing a geostatistical interpolation technique such as kriging.
The accuracy order of quadrature formulas can be improved by utilizing the Richardson extrapolation method to take the terms away.
In other words, classification accuracy cannot be improved by utilizing the two new features and discarding the original features.
These results could be improved by utilizing a higher number of codes, but this will result in an increased complexity.
The maximum achievable throughput and in turn the throughput performance of e-TICA2 can be improved by utilizing all four radios of the gateway.
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The results of the optimization are improved by utilizing data from process simulation.
Both the SSNR and the LSD are improved by utilizing the LTP with the modified pitch lag detector in Eq. (13).
Considering that the underlying network topology of VANETs is determined by roads and by the characteristics of vehicular traffic on the roads, the performance of many forwarding strategies is improved by utilizing these properties.
The performance is improved by utilizing F-kNN and SVM as the classifiers.
For the original networks to be improved by the proposed algorithm, we utilized GeneNet [ 37, 38] based on an empirical graphical Gaussian model (GGM) and G1DBN [ 39] based on dynamic Bayesian networks using first order conditional dependencies.
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