ArticleSensors (Basel, Switzerland)2020
A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals' Quality.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets.Bioengineering (Basel, Switzerland) · 2021Article
- Acquisition and Processing of Brain Signals.Sensors (Basel, Switzerland) · 2021Article
- Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset.Bioengineering (Basel, Switzerland) · 2021Article
- A Machine Learning Approach for the Automatic Estimation of Fixation-Time Data Signals' Quality.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Fixation time measures have been widely adopted in studies with infants and young children because they can successfully tap on their meaningful nonverbal behaviors. While recording preverbal children's behavior is relatively simple, analysis of collected signals requires extensive manual preprocessing. In this paper, we investigate the possibility of using different Machine Learning (ML)-a Linear SVC, a Non-Linear SVC, and K-Neighbors-classifiers to automatically discriminate between
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.