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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the mobile communication field, some of the video applications boosted theinterest of robust methods for video quality assessment. Out of all existingmethods, We Preferred, No Reference Video Quality Assessment is the one whichis most needed in situations where the handiness of reference video ispartially available. Our research interest lies in formulating and meldingeffective features into one model based on human visualizing characteristics.Our work explores comparative study between Supervised and unsupervisedlearning methods. Therefore, we implemented support vector regression algorithmas NR-based Video Quality Metric(VQM) for quality estimation with simplifiedinput features. We concluded that our proposed model exhibited sparseness evenafter dimension reduction for objective scores of SSIM quality metric.

Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications