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Data Envelopment Analysis (DEA) is the dominant non-parametric approach to evaluate the efficiency of Decision-Making Units (DMU).
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a Environment 1. b Environment 2. c Environment 3. Running time is chosen to evaluate the efficiency of the OPMPD approach and the eigenvalue-based approach.
We tested the quality of recommendations using human evaluation technique, and we presented the results of some experimental tests used to evaluate the efficiency of the approach.
Hereafter, we present a set of conducted experiments to evaluate the efficiency of the approach.
For instance, Zhu (1998) used principal component analysis (PCA) approach for aggregating multiple inputs and multiple outputs to evaluate the efficiency of DEA models.
To evaluate the efficiency of the approach, this list was ordered according to the ratios of E-values between the averaged Brassicaceae and AM-competent dicot plants, respectively (Additional file 14: Table S6).
In order to evaluate the efficiency of the proposed approach, we developed the prototype RECQUERY (RECommend QUERY).
To evaluate the efficiency of the proposed approach, some experiments have been conducted using a network simulator provided by the 3GPP video ad hoc group [12].
A Monte Carlo sampling approach was implemented using a newly developed isotopomer model to evaluate the efficiency of different carbon labeling patterns toward determining reaction fluxes in E. coli.
For this study, 20 individual DNA samples and 4 pooled DNA samples were analysed using the Illumina Infinium HumanMethylation450 BeadChip array to evaluate the efficiency of the pooling approach in EWAS studies.
To evaluate the efficiency of these proposed approaches, several problems are solved using SA and TS, and then the related results are compared.
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