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The results show that PMS model achieves more standard vertical descent profiles over vectoring.
Although the proposed model achieves more hiding rate, Gifshuffle is computationally faster than other techniques.
Evaluation results of the system indicate that the proposed prediction model achieves more accurate disaster determination than the conventional method.
The comparison results with the MLP method show that the MLBP prediction model achieves more satisfactory results, and it is more reliable and effective to solve the problem.
In addition, our model achieves more robustness across domains than the STRUCT (e.g. 7.47% of the difference in performance between the two domains for our model vs. 18.8% for the STRUCT).
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The present model achieved more than 97% accuracy in classifying the flow patterns for wide range of flow conditions.
A direct comparison of our empirical method with distribution model-based methods confirmed that our model achieved more accurate results.
Further research is needed to evaluate whether existing models achieve more than the ever improving usual care to improve outcomes of importance to patients.
Also, when applying independent selection, the outage probability is always better than dis-joint selection model since it achieves more selection diversity, but at the price of some practical implementation issues that may be caused by allowing one relay receiving (or sending) from (to) different nodes and also higher computation complexity.
Our proposed model, compared to WWF, achieves more efficient resource allocation in terms of system throughput and resources utilization.
We also find that the pretrained DNN based model could achieve more than 90% on specificity, meanwhile the GMM-HMM model could only get about 77%.
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