Affective Computing Applications using Artificial Intelligence in Healthcare
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Affective Computing Applications using Artificial Intelligence in Healthcare
Methods, approaches and challenges in system design
Murugappan, M.
Institution of Engineering and Technology
07/2024
214
Dura
9781839537318
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Chapter 1: EEG-based emotion recognition using time-frequency images and hybrid ResNet models
Chapter 2: Detection of facial emotion using thermal imaging based on deep learning techniques
Chapter 3: Gender and emotion recognition from EEG and eye movement patterns
Chapter 4: Gesture-oriented supernumerary robotic fingers for post-stroke rehabilitation
Chapter 5: Comparative analysis of CNN and RNN-LSTM model-based depression detection using modified spectral and acoustic features
Chapter 6: Unraveling emotions: harnessing pre-trained convolutional neural networks for electroencephalogram signal analysis
Chapter 7: Deep neural network-based stress detection using biosignals
Chapter 8: Explainable deep learning models for emotion recognition using facial images
Chapter 9: Converging emotion recognition with AI and IoT
Chapter 2: Detection of facial emotion using thermal imaging based on deep learning techniques
Chapter 3: Gender and emotion recognition from EEG and eye movement patterns
Chapter 4: Gesture-oriented supernumerary robotic fingers for post-stroke rehabilitation
Chapter 5: Comparative analysis of CNN and RNN-LSTM model-based depression detection using modified spectral and acoustic features
Chapter 6: Unraveling emotions: harnessing pre-trained convolutional neural networks for electroencephalogram signal analysis
Chapter 7: Deep neural network-based stress detection using biosignals
Chapter 8: Explainable deep learning models for emotion recognition using facial images
Chapter 9: Converging emotion recognition with AI and IoT
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Chapter 1: EEG-based emotion recognition using time-frequency images and hybrid ResNet models
Chapter 2: Detection of facial emotion using thermal imaging based on deep learning techniques
Chapter 3: Gender and emotion recognition from EEG and eye movement patterns
Chapter 4: Gesture-oriented supernumerary robotic fingers for post-stroke rehabilitation
Chapter 5: Comparative analysis of CNN and RNN-LSTM model-based depression detection using modified spectral and acoustic features
Chapter 6: Unraveling emotions: harnessing pre-trained convolutional neural networks for electroencephalogram signal analysis
Chapter 7: Deep neural network-based stress detection using biosignals
Chapter 8: Explainable deep learning models for emotion recognition using facial images
Chapter 9: Converging emotion recognition with AI and IoT
Chapter 2: Detection of facial emotion using thermal imaging based on deep learning techniques
Chapter 3: Gender and emotion recognition from EEG and eye movement patterns
Chapter 4: Gesture-oriented supernumerary robotic fingers for post-stroke rehabilitation
Chapter 5: Comparative analysis of CNN and RNN-LSTM model-based depression detection using modified spectral and acoustic features
Chapter 6: Unraveling emotions: harnessing pre-trained convolutional neural networks for electroencephalogram signal analysis
Chapter 7: Deep neural network-based stress detection using biosignals
Chapter 8: Explainable deep learning models for emotion recognition using facial images
Chapter 9: Converging emotion recognition with AI and IoT
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
