Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Ömer F. Alçin"'
Publikováno v:
Brain Informatics, Vol 7, Iss 1, Pp 1-12 (2020)
Abstract In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-ba
Externí odkaz:
https://doaj.org/article/cc7752225e2f4b3faeab56293dddbc23
Publikováno v:
Computers in biology and medicine. 143
Autism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone t
In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG based emotion classification. Emotion recognition is important for human-machine interactions. Facial-features and body-gestures based approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69844ad88d373670f4063407654c74fc
https://doi.org/10.21203/rs.3.rs-37469/v1
https://doi.org/10.21203/rs.3.rs-37469/v1
Publikováno v:
Energies, Vol 12, Iss 8, p 1449 (2019)
This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine
Externí odkaz:
https://doaj.org/article/7963a37b1266448fa6e52e4125a63382
Publikováno v:
Energies, Vol 11, Iss 1, p 145 (2018)
Monitoring Power Quality Events (PQE) is a crucial task for sustainable and resilient smart grid. This paper proposes a fast and accurate algorithm for monitoring PQEs from a pattern recognition perspective. The proposed method consists of two stages
Externí odkaz:
https://doaj.org/article/dfe5608a0c414ff8bacdf479bc66bd08