Geofrey Kansiime, Marjorie Sarah Kabuye Batiibwe
Adoption of online mathematics learning in Ugandan government universities during the COVID-19 pandemic: pre-service teachers' behavioural intention and challenges
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emotion human sense influence individuals life quality positive negative ways ability distinguish different types emotion lead researchers estimate current situation patients probability future disease recognizing emotions images problems concealing feeling modifying facial expressions led researchers consider electroencephalography eeg signals accurate emotion detection however complexity eeg recordings data analysis using conventional machine learning algorithms caused inconsistent emotion recognition therefore utilizing hybrid deep learning models techniques become common due ability analyze complicated data achieve higher performance integrating diverse features models however researchers prioritize models fewer parameters achieve highest average accuracy study improves convolutional fuzzy neural network cfnn emotion recognition using eeg signals achieve reliable detection system initially preprocessing feature extraction phases implemented obtain noiseless informative data cfnn modified architecture trained classify emotions several parametric comparative experiments performed proposed model achieved reliable performance emotion recognition average accuracy 9821 9808 valence pleasantness arousal intensity respectively outperformed stateoftheart methods