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A Predictive Model Emotion Recognition on Deep Learning and Shallow Learning Techniques Using EEG Signal
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Author(s): Vidhya R. (SRM Institute of Science and Technology, India), Sandhia G. K. (SRM Institute of Science and Technology, India), Jansi K. R. (SRM Institute of Science and Technology, India), Nagadevi S. (SRM Institute of Science and Technology, India)and Jeya R. (SRM Institute of Science and Technology, India)
Copyright: 2023
Pages: 8
Source title:
Principles and Applications of Socio-Cognitive and Affective Computing
Source Author(s)/Editor(s): S. Geetha (Vellore Institute of Technology, Chennai, India), Karthika Renuka (PSG College of Technology, India), Asnath Victy Phamila (Vellore Institute of Technology, Chennai, India)and Karthikeyan N. (Syed Ammal Engineering College, India)
DOI: 10.4018/978-1-6684-3843-5.ch004
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Abstract
Social, psychological, and emotional well-being are all aspects of mental health. Mental illness can cause problems in daily life, physical health, and interpersonal connections. Severe changes in education, attitude, or emotional management of students cause suffering are defined as children's mental disorders. Artificial intelligence (AI) technology has lately been advanced to help intellectual fitness professionals, especially psychiatrists and clinicians, in making choices primarily based totally on affected person records along with medical history, behavioural records, social media use, and so on. There is a pressing need to address core mental health concerns in children, which can progress to more serious problems if not addressed early. As a result, a shallow learning technique-assisted integrated prediction model (SLIPM) has been presented in this research to predict and diagnose mental illness in children early. Convolutional neural networks (CNN) are built first in the proposed model to learn deep-learned patient behavioural data characteristics.
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