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/

Zendy Plus

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

Zendy Tools

Zendy Open

Depression has been the leading cause of mental-health illness worldwide.Major depressive disorder (MDD), is a common mental health disorder thataffects both psychologically as well as physically which could lead to loss oflives. Due to the lack of diagnostic tests and subjectivity involved indetecting depression, there is a growing interest in using behavioural cues toautomate depression diagnosis and stage prediction. The absence of labelledbehavioural datasets for such problems and the huge amount of variationspossible in behaviour makes the problem more challenging. This paper presents anovel multi-level attention based network for multi-modal depression predictionthat fuses features from audio, video and text modalities while learning theintra and inter modality relevance. The multi-level attention reinforcesoverall learning by selecting the most influential features within eachmodality for the decision making. We perform exhaustive experimentation tocreate different regression models for audio, video and text modalities.Several fusions models with different configurations are constructed tounderstand the impact of each feature and modality. We outperform the currentbaseline by 17.52% in terms of root mean squared error.

Multi-level Attention network using text, audio and video for Depression Prediction