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We introduce a novel context-sensitive feature extraction approach for spontaneous speech recognition.

Ciznicki et al. [6] presented an isosurface extraction approach for CT and MRI images that combines the marching tetrahedra algorithm with histogram pyramids [10, 50] using multiple GPUs.

There have been a number of studies using the automated solvent extraction approach for laboratory-scale pretreatment assays using hot water [ 25] and dilute acid [ 26, 27] pretreatment chemistries.

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Information extraction approaches for specific clinical subdomains have been in the focus of research for several years, e.g., to extract smoking status [ 27].

Other previously used feature extraction approaches for epilepsy diagnosis include empirical mode decomposition (EMD), multilevel Fourier transform (FT), and orthogonal matching pursuit [ 5– 9].

Accordingly, future research lines are also intended to demonstrate the suitability of the proposed feature extraction approach from OCT images for tissue classification in other medical applications, specifically urology.

This fourth level is character level feature extraction approach used for extracting features of each character window from given word (Table 1).

Their entity extraction approach accounted for direct identification of elements and included a list of stop words to allow for improved precision.

In the present research work, original QuEChERS extraction approach was modified for investigation of pinoxaden herbicides in different matrices such as water, grains, and agricultural soil samples.

Automating the extraction of cloud cost models would reduce error and time, and prevent lock-in [69, 70], so another area of future work is the creation of a model extraction approach and tool for our cloud cost models.

We show that the accuracy as well as processing speed of the proposed semantic relation extraction approach is sufficient for its large scale application on biomedical text.

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