373,485 research outputs found
A Novel Multidimensional Reference Model For Heterogeneous Textual Datasets Using Context, Semantic And Syntactic Clues
With the advent of technology and use of latest devices, they produces
voluminous data. Out of it, 80% of the data are unstructured and remaining 20%
are structured and semi-structured. The produced data are in heterogeneous
format and without following any standards. Among heterogeneous (structured,
semi-structured and unstructured) data, textual data are nowadays used by
industries for prediction and visualization of future challenges. Extracting
useful information from it is really challenging for stakeholders due to
lexical and semantic matching. Few studies have been solving this issue by
using ontologies and semantic tools, but the main limitations of proposed work
were the less coverage of multidimensional terms. To solve this problem, this
study aims to produce a novel multidimensional reference model using
linguistics categories for heterogeneous textual datasets. The categories such
context, semantic and syntactic clues are focused along with their score. The
main contribution of MRM is that it checks each tokens with each term based on
indexing of linguistic categories such as synonym, antonym, formal, lexical
word order and co-occurrence. The experiments show that the percentage of MRM
is better than the state-of-the-art single dimension reference model in terms
of more coverage, linguistics categories and heterogeneous datasets.Comment: International Journal of Advanced Science and Applications, Volume
14, Issue 10, pp. 754-763, 202
Fuzzy Fibers: Uncertainty in dMRI Tractography
Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research
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