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We present two different simulated datasets to demonstrate the performance of FID3B (Figs. 1 and 2).
The model is also tested on large synthetic datasets to demonstrate its remarkable scalability.
We have conducted a series of experiments on two publicly available datasets to demonstrate the effectiveness of the proposed system.
The performance of the model is examined using two benchmark datasets to demonstrate the effectiveness of the model.
Furthermore, we have implemented two baseline algorithms and used two kinds of datasets to demonstrate the efficiency and scalability of our algorithm.
We instantiate the OoEVV system with a small number of functional Magnetic Resonance Imaging datasets, to demonstrate the system's ability to describe the variables of a neuroimaging experiment.
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For ease and efficiency of generating a dataset to demonstrate the concept used in the interface, this information was extracted and created manually from the existing archives.
This DFEs-based approach offers faster calculation even in the case when there is only a single dataset to demonstrate the principles.
This study includes a "leave-one-class-out" experiment on the pendigits dataset to demonstrate the detection ability of the proposed OOR method for hidden classes.
Also, we used this dataset to demonstrate that some popular functions designed for representing the high-viscosity segment (where the activation energy changes with temperature) are not recommendable for approximating the low-viscosity segment (where the activation energy is constant).
We also evaluated the performance of tests by analyzing a real sequence dataset to demonstrate the utility of the proposed tests.
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