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Subsequently, the problem of using one, fixed disturbance distribution matrix is eliminated by using the interacting multiple models algorithm to select an appropriate unknown input distribution matrix from a predefined set of matrices.
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The ordinary sensor node runs automatic ARIMA modeling algorithm to build ARIMA prediction model automatically.
The authors in [3] present a multiple model algorithm to eliminate the range ambiguity problem in an HPRF radar.
Typically, to apply a QSAR model the user has to calculate chemical descriptors, then apply the model algorithm to get the result [13].
In this article, we propose a new spring-model algorithm to resolve the problems mentioned above with the presence of anchors.
In this paper, we consider a hybrid model which employs SVM to model the KF output that represents the linear error and FOS used as AR modeling algorithm to model the nonlinear errors.
The automatic ARIMA modeling-based data aggregation scheme works as follows: First of all, the ordinary sensor node runs automatic ARIMA modeling algorithm to build an appropriate ARIMA prediction model.
I use a lottery model algorithm to form the scale-free networks, i.e., the probability of an existing gene acquiring a connection to a new gene is proportional to the number of existing connections [57].
Third, by using the e-model algorithm to select our validation cohort, we were unable to evaluate how individual data elements performed in identifying diabetes.
Using multiple independent INDEL calling pipelines with machine learning based consensus model algorithm to produce a consensus call set we were able to maximize the consensus call set's sensitivity and specificity.
This problem is NP-hard [ 33], and thus it is unlikely that there exists an efficient (polynomial-time in the size of the model) algorithm to obtain a globally optimal solution.
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CEO of Professional Science Editing for Scientists @ prosciediting.com