English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper has been withdrawn by the authors. Cellular network data hasbecome a hot source of study for extraction of user-mobility andspatio-temporal trends. Location binding in mobility data can be done throughdifferent methods like GPS, service provider assisted faux-GPS and Cell GlobalIdentity (CGI). Among these Cell Global Identity is most inexpensive method andreadily available solution for mobility extraction; however exact spatialextraction is somehow a problem in it. This paper presents the spatialextraction technique of mobile phone user raw data which carries theinformation like location information, proximity location and activity ofsubjects. This work mainly focuses on the data pre-processing methodology andtechnique to interpret the low level mobility data into high level mobilityinformation using the designed clustering methodology and publically availableCell-IDs databases. Work proposed the semi- supervised strategy to derive themissing locations thorough the usage of semantic tag information and removal ofspatial outliers for precise mobility profile building.

Precise Location Acquisition of Mobility Data Using Cell-id