Sentence examples for imprecision in data from inspiring English sources

The imprecision in data can often be represented by triangular fuzzy numbers.

However, this potential is limited by the challenges of the sheer size and complexity of high-throughput data resources, often resulting in significant imprecision in data usage.

In addition to discordant counts, problems were lack of provision of crude death numbers even when death was an outcome of interest (Cases 1 and 3), imprecision in data entry (Case 4), reporting of deaths under serious adverse events without specification as to whether they were counted as part of the death outcome (Case 4) or the participant flow (Case 7).

The report cautions against ranking individual schools against each other at the top and bottom of its debt ranges, due to imprecision in the data, but it does identify the colleges it is confident fall into those ranges.

We propose a general fuzzy optimization model based on chanced constrained optimization to design recommendation systems that can take into consideration (i) the imprecision in the data and (ii) the imprecision by which one can estimate the effect of a recommendation on the user of the system.

Imprecision in the data from adults was unlikely, as over 14 000 participants were included in trials of at least six months' duration and effect sizes were highly statistically significant.

Although there was no imprecision for adults, imprecision was high in data from the children (although not quantifiable), and pooling was not possible.

The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties.

The inherent noise, inaccuracy, and imprecision in the sensory data [13, 18] cause significant drift (from cumulative errors) in estimated location, or trajectory even the smartphone is fixed inside the vehicle.

In contrast to these novel solutions, the main challenges in estimating the location (or trajectory) using inertial sensors include: 1) The inherent noise, inaccuracy, and imprecision in the sensory data [13, 18] cause significant drift (from cumulative errors) in estimated location, or trajectory even the smartphone is fixed inside the vehicle.

The advantages of this approach include the following: modelling of non-linear behaviour; accommodation of imprecision in the normalisation of the data; aggregation without subjective allocation of weights to the indicators; ranking of alternatives in such a way that the output value can be treated as the health value or integrity index.