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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In recent years, mobile networks and its applications are developing rapidly. Therefore, the issue to ensure quality of service (QoS) is a key issue for the service providers. The movement prediction of Mobile Users (MUs) is an important problem in cellular communication networks. The movement prediction applications of MUs include automatic bandwidth adjustment, smart handover, location based services,… In this work, we propose two new algorithms named the Find_UMP_1 algorithm and the Find_UMP_2 algorithm for mining the next movements of the mobile users. In the Find_UMP_1 algorithm, we make to reduce the complexity of the traditional UMPMining algorithm. In the Find_UMP_2 algorithm, we perform to reduce the number of transactions of the User Actual Paths (UAPs) database. The results of our experiments show that our proposed algorithms outperform the traditional UMPMining algorithm in terms of the execution time. In addition, we also propose the UMP_Online algorithm in order to reduce the execution time as adding new data. The benefit of applying the UMP_Online algorithm is that the system can run online in real time. Therefore, we can perform the applications effectively

Mobility Patterns Mining Algorithms with Fast Speed