Sentence examples for location extraction from inspiring English sources

The location extraction component is responsible for extracting physical locations from location traces collected by the application.

To evaluate the effect of different challenge questions on user performance in real‐life, we implemented a location‐based authentication framework that has four main components, namely, the location tracking component, the location extraction component, the question and answer generation component, and finally, the authentication component.

As users may not place the marker on exactly the same location coordinates estimated/identified by the location extraction component, there is an error tolerance (e.g., 100 meters great circle distance) in our system, which is calculated based on the Haversine distance between the selected coordinates and the estimated location.

RDH having great performance on DE and HS techniques to retain location map, extraction information, image restoration and overflow/underflow problems [17].

The toponym (i.e., location name) extraction and resolution phases are carried out using the off-the-shelf geoparser, CLAVIN [56].

The location for extraction of total metagenomic DNA should be selected which may include hydrocarbon, pesticides or heavy metal-contaminated agricultural soil and/or rhizosphere.

These secondary forests are ideal locations for extraction of forest products that fit within the cycle of fallow regeneration in areas near human settlements.

To improve comparability across studies, investigators need to provide sufficient detail on the population sampled, the collection methodology, pesticides of interest, selection of sampling locations, chemical extraction, and analytical methods, as well as the statistical measures used to address values below the analytical detection limits.

Based on host types and/or geographic locations, these extractions were further merged into 16 pools for RNA-seq library construction and sequencing (Table 1).

Their quality is largely affected by the weather conditions, the extraction location along the plant and the techniques used to extract them (retting, bleaching, etc).

In this project, three different CNNs (recognition, location, and feature extraction) were established to accomplish the aforementioned tasks automatically.