AIMC Topic: Emotions

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Nostalgia encourages exploration and fosters uncertainty in response to AI technology.

The British journal of social psychology
The burgeoning progress of cutting-edge technology paradoxically evokes nostalgia. How does this emotion influence responses to innovative technology, such as Artificial Intelligence (AI)? We hypothesized that two pathways operate concurrently. First...

Evaluating the Impact of BoNT-A Injections on Facial Expressions: A Deep Learning Analysis.

Aesthetic surgery journal
BACKGROUND: Botulinum toxin type A (BoNT-A) injections are widely administered for facial rejuvenation, but their effects on facial expressions remain unclear.

Crucial rhythms and subnetworks for emotion processing extracted by an interpretable deep learning framework from EEG networks.

Cerebral cortex (New York, N.Y. : 1991)
Electroencephalogram (EEG) brain networks describe the driving and synchronous relationships among multiple brain regions and can be used to identify different emotional states. However, methods for extracting interpretable structural features from b...

Unveiling the Potential of ChatGPT and YOLOv7 for Evaluating Children's Emotions Using Their Artistic Expressions.

Studies in health technology and informatics
Recent advancements in large language models (LLMs) have sparked considerable interest in their potential applications across various healthcare domains. One promising prospect is leveraging these generative models to accurately predict children's em...

Improved optimizer with deep learning model for emotion detection and classification.

Mathematical biosciences and engineering : MBE
Facial emotion recognition (FER) is largely utilized to analyze human emotion in order to address the needs of many real-time applications such as computer-human interfaces, emotion detection, forensics, biometrics, and human-robot collaboration. Non...

A Sentiment Pre-trained Text-Guided Multimodal Cross-Attention Transformer for Improved Depression Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Depression is a widespread mental health issue requiring efficient automated detection methods. Traditional single-modality approaches are less effective due to the disorder's complexity, leading to a focus on multimodal analysis. Recent advancements...

Emotion Recognition from Speech Signals by Mel-Spectrogram and a CNN-RNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Speech emotion recognition (SER) in health applications can offer several benefits by providing insights into the emotional well-being of individuals. In this work, we propose a method for SER using time-frequency representation of the speech signals...

Assessing Basic Emotion via Machine Learning: Comparative Analysis of Number of Basic Emotions and Algorithms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper explores the use of machine learning (ML) methods to identify "clusters" of basic emotions based on pleasure, arousal, and dominance (PAD). The data was obtained from the Dataset for Emotion Analysis using Physiological Signals (DEAP), dat...

EmoNet: Deep Learning-based Emotion Climate Recognition Using Peers' Conversational Speech, Affect Dynamics, and Physiological Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Understanding the emotional dynamics within social interactions is crucial for meaningful interpretation. Despite progress in emotion recognition systems, recognizing the collective emotional climate among peers has been understudied. Addressing this...

Contrastive Self-supervised EEG Representation Learning for Emotion Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that applying self-supervision to physiological signals can yield better representations of the signals. In...