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

With the tremendous advance of technologies in data collection and storage, sequential data are becoming more and more ubiquitous in a wide spectrum of application scenarios. There are various embodiments of sequential data such as time series/video frames, biological data, and event data. It bears important practical utility for learning and understanding the dynamic behavior as well as the causality relationships across sequences, and it also calls for robust models to handle noisy and incomplete sequence data in real-world settings. Thus, machine-learning-based methods can be applied to efficiently analyze and model these sequential data.

IEEE Access Special Section: Sequential Data Modeling and Its Emerging Applications