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Our empirical experiments on biometrics fusion show strong evidences regarding the potential of the proposed method.
The model, together with the background knowledge reviewed in the paper, represents a framework that can be used to guide the design and interpretation of empirical experiments on IMs, and to computationally validate and further develop theories on them.
Empirical experiments on some of datasets of the national institute of standards and technology (NIST), fingerprint verification competition (FVC), and THUPALMLAB databases show that the proposed algorithm considerably narrows down the search space in fingerprint and palmprint databases and increase the recognition performance.
The open source Java implementation of IsoEM is freely available at http://dna.engr.uconn.edu/software/IsoEM/. Empirical experiments on both synthetic and real RNA-Seq datasets show that IsoEM has scalable running time and outperforms existing methods of isoform and gene expression level estimation.
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Furthermore, we have conducted an empirical experiment on six public and real non-encoded datasets.
The adjustments in QP are based on empirical experiment to avoid negative target bits as much as possible.
Empirical experiments were conducted on both analytically unimodal and multimodal test functions.
Also, there is a lack of systems which facilitate the work of a data analyst in anonymizing this type of data structures and performing empirical experiments in a controlled manner on different datasets.
The methodology was based on a quantitative approach that used empirical experiments in an actual-sized room.
Lastly, we complement our theoretical analyses with supporting empirical experiments.
It involves the use of empirical experiments and thus satisfies the scientific standards we nowadays expect of an empirical science.
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CEO of Professional Science Editing for Scientists @ prosciediting.com