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

Physiological measurements involves observing variables that attribute to thenormative functioning of human systems and subsystems directly or indirectly.The measurements can be used to detect affective states of a person with aimssuch as improving human-computer interactions. There are several methods ofcollecting physiological data, but wearable sensors are a common, non-invasivetool for accurate readings. However, valuable information is hard to extractfrom the raw physiological data, especially for affective state detection.Machine Learning techniques are used to detect the affective state of a personthrough labeled physiological data. A clear problem with using labeled data iscreating accurate labels. An expert is needed to analyze a form of recording ofparticipants and mark sections with different states such as stress and calm.While expensive, this method delivers a complete dataset with labeled data thatcan be used in any number of supervised algorithms. An interesting questionarises from the expensive labeling: how can we reduce the cost whilemaintaining high accuracy? Semi-Supervised learning (SSL) is a potentialsolution to this problem. These algorithms allow for machine learning models tobe trained with only a small subset of labeled data (unlike unsupervised whichuse no labels). They provide a way of avoiding expensive labeling. This papercompares a fully supervised algorithm to a SSL on the public WESAD (WearableStress and Affect Detection) Dataset for stress detection. This paper showsthat Semi-Supervised algorithms are a viable method for inexpensive affectivestate detection systems with accurate results.

Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data