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

Today, in the artificial intelligence research field, Deep Learning (DL) is one of the fastestgrowing techniques because of the power of learn ing features, that gives a higher level of abstraction of the raw attributes; and related research in Recurrent Neural Networks (RNN) and Long Short -Term Memory Networks (LSTM) have shown exemplary results in neural machine translation, neural image caption generation, NLP and so on. For our research, in order to detect potential problems and to facilitate proactive management, we focus on predictive process monitoring (PPM) as domain area, by predict ing business behaviour from h istorical event logs. Recent research works, LSTM networks have gained attention in PPM and have been proved that they can highly improve prediction accuracy in PPM. According to the literature, we have learned that PPM resembles to an early sequence classificat ion problem in NLP. And, recent trends in DL based NLP, attention mechanism is mostly embedded in neural networks. Inspired by these results, this paper proposes to firstly use Attention Based LSTM with Multi Tasks Learning for PPM.

Attention Based LSTM with Multi Tasks Learning for Predictive Process Monitoring