Home ›
Entities
› academia
› Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection
Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection
Research article (Biomedical Signal Processing and Control, 2024) · cited 14× · AI/ML
Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection
Summary
Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection is a scholarly article[1].
Key Facts
Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection. Retrieved May 24, 2026, from https://4ort.xyz/entity/towards-mental-load-assessment-for-high-risk-works-driven-by-psychophysiological-data-combining-a-1d-cnn-model-with-rand
MLA“Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/towards-mental-load-assessment-for-high-risk-works-driven-by-psychophysiological-data-combining-a-1d-cnn-model-with-rand.
BibTeX@misc{4ortxyz_towards-mental-load-assessment-for-high-risk-works-driven-by-psychophysiological-data-combining-a-1d-cnn-model-with-rand_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection}}, year = {2026}, url = {https://4ort.xyz/entity/towards-mental-load-assessment-for-high-risk-works-driven-by-psychophysiological-data-combining-a-1d-cnn-model-with-rand}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Towards mental load assessment for high-risk works driven by psychophysiological data: Combining a 1D-CNN model with random forest feature selection — https://4ort.xyz/entity/towards-mental-load-assessment-for-high-risk-works-driven-by-psychophysiological-data-combining-a-1d-cnn-model-with-rand (retrieved 2026-05-24)
