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Emotions

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DSTCNet: Deep Spectro-Temporal-Channel Attention Network for Speech Emotion Recognition.

IEEE transactions on neural networks and learning systems
Speech emotion recognition (SER) plays an important role in human-computer interaction, which can provide better interactivity to enhance user experiences. Existing approaches tend to directly apply deep learning networks to distinguish emotions. Amo...

An EEG-based emotion recognition method by fusing multi-frequency-spatial features under multi-frequency bands.

Journal of neuroscience methods
BACKGROUND: Recognition of emotion changes is of great significance to a person's physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Theref...

Sentiment analysis of tweets employing convolutional neural network optimized by enhanced gorilla troops optimization algorithm.

Scientific reports
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the a...

Direct perception of affective valence from vision.

Nature communications
Subjective feelings are thought to arise from conceptual and bodily states. We examine whether the valence of feelings may also be decoded directly from objective ecological statistics of the visual environment. We train a visual valence (VV) machine...

The public mental representations of deepfake technology: An in-depth qualitative exploration through Quora text data analysis.

PloS one
The advent of deepfake technology has raised significant concerns regarding its impact on individuals' cognitive processes and beliefs, considering the pervasive relationships between technology and human cognition. This study delves into the psychol...

EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Scientific reports
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effec...

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Neural networks : the official journal of the International Neural Network Society
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cogniti...

Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.

JMIR mental health
BACKGROUND: The rise of wearable sensors marks a significant development in the era of affective computing. Their popularity is continuously increasing, and they have the potential to improve our understanding of human stress. A fundamental aspect wi...

Naturalistic multimodal emotion data with deep learning can advance the theoretical understanding of emotion.

Psychological research
What are emotions? Despite being a century-old question, emotion scientists have yet to agree on what emotions exactly are. Emotions are diversely conceptualised as innate responses (evolutionary view), mental constructs (constructivist view), cognit...

Personalized Clustering for Emotion Recognition Improvement.

Sensors (Basel, Switzerland)
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense,...