Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering

Emotion Recognition in Gaming Dataset to Reduce Artifacts in the Self-Assessed Labeling Using Semi-Supervised Clustering

Popular comments suggest that continuous exposure of children and adolescents to video games yields a non-benefit behavior in the players’ mental health. Contrarily, several studies have proven that commercial and serious games improve mental activity; some are used in treating psychologi...

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Bibliographic Details
Main Authors: Oscar Almanza-Conejo, Juan Gabriel Avina-Cervantes, Arturo Garcia-Perez, Mario Alberto Ibarra-Manzano
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Emotion recognition
gaming
conflict learning
clustering
machine learning
Online Access:https://ieeexplore.ieee.org/document/10496111/
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