Our experimental results show that the performance prediction model achieves high accuracy and the mean absolute error is 2.83%.
Our experimental results show that the performance prediction model achieves high accuracy and the smart VM scheduling algorithms based on the prediction improves system efficiency and VM performance stability.
Also, while the Bayesian classifier based model achieves high accuracy, the model requires training data for both legitimate and adversarial users, which may not be always available in real-life.
Compared with existing approaches, experimental results on synthetic and real-world data sets demonstrate that the proposed model achieve higher accuracy and efficiency.
With the introduction of elastic strain energy, a dominant concept of interstitial diffusion theory, we are able to train the machine learning model achieving high accuracy of R-squared 0.9 without being over-fitted by using 94 impurity – host binary systems reported in the literature.
The predictive model achieved high accuracy evaluated by 10-fold cross-validations.
In this study, the PQ-concentrations of 41 patients before HP treatment were employed as variable of input layer, and the model achieved high accuracy by selecting the PQ-concentrations after HP treatment as test.
All models achieve higher accuracy after grid search recalibration, and we show that many of the previously proposed LW models collapse into only a few different families of models.
Both models achieve higher accuracy than BLAST scores alone (except for the CSSS model with short 100 bp fragments).
Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.
These interpretative models exhibit different trade-offs between completeness and accuracy the spoke model achieves high accuracy at the cost of incompleteness, whereas the matrix model provides a more complete model but relatively low accuracy due to pairing all prey proteins from a given bait with each other.
