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

Introduction. We investigate the generalization ability of models built ondatasets containing a small number of subjects, recorded in single studyprotocols. Next, we propose and evaluate methods combining these datasets intoa single, large dataset. Finally, we propose and evaluate the use of ensembletechniques by combining gradient boosting with an artificial neural network tomeasure predictive power on new, unseen data.  Methods. Sensor biomarker data from six public datasets were utilized in thisstudy. To test model generalization, we developed a gradient boosting modeltrained on one dataset (SWELL), and tested its predictive power on two datasetspreviously used in other studies (WESAD, NEURO). Next, we merged four smalldatasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of99 subjects,. In addition, we utilized random sampling combined with anotherdataset (EXAM) to build a larger training dataset consisting of 200 synthesizedsubjects,. Finally, we developed an ensemble model that combines our gradientboosting model with an artificial neural network, and tested it on twoadditional, unseen publicly available stress datasets (WESAD and Toadstool).  Results. Our method delivers a robust stress measurement system capable ofachieving 85% predictive accuracy on new, unseen validation data, achieving a25% performance improvement over single models trained on small datasets.  Conclusion. Models trained on small, single study protocol datasets do notgeneralize well for use on new, unseen data and lack statistical power.Ma-chine learning models trained on a dataset containing a larger number ofvaried study subjects capture physiological variance better, resulting in morerobust stress detection.

Ensemble Machine Learning Model Trained on a New Synthesized Dataset Generalizes Well for Stress Prediction Using Wearable Devices