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We propose an adaptive data acquisition technique that depends on the object to be imaged in magnetic resonance (MR) imaging.
This paper proposes an adaptive data center activation model that consolidates adaptive activation of switches and hosts simultaneously integrated with a statistical request prediction algorithm.
Further, an adaptive data collection technique is integrated that is able to provide evidence about the effectiveness of the groundwater transport model in use.
This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company's future performance.
We designed an adaptive data center activation model by using a cognitive cycle composed of three steps: data collection, prediction, and activation.
In this paper, we propose an adaptive data replication algorithm, called the Popular File Replicate First algorithm (PFRF for short), which is developed on a star-topology data grid with limited storage space based on aggregated information on previous file accesses.
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It uses an iterative filters strategy together with an adaptive, data-driven filter length selection to achieve the necessary decomposition.
Furthermore, it yields a compromise between Ridge and LASSO regressions, and hence combines a weak (l2-norm) and strong (l1-norm) shrinkage, in an adaptive, data-driven manner.
Jin et al. presented an adaptive data-gathering mechanism based on compressive sensing.
We propose an adaptive, data-driven thresholding method based on a recently developed idea of Minimum Noiseless Description Length (MNDL).
In order to reduce the measurements, we present a data prediction method based on neural networks which performs an adaptive, data-driven, and non-uniform sampling.
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