AIMC Topic: Emotions

Clear Filters Showing 391 to 400 of 895 articles

Effective Prediction and Important Counseling Experience for Perceived Helpfulness of Social Question and Answering-Based Online Counseling: An Explainable Machine Learning Model.

Frontiers in public health
The social question answering based online counseling (SQA-OC) is easy access for people seeking professional mental health information and service, has become the crucial pre-consultation and application stage toward online counseling. However, ther...

Unconscious Other's Impression Changer: A Method to Manipulate Cognitive Biases That Subtly Change Others' Impressions Positively/Negatively by Making AI Bias in Emotion Estimation AI.

Sensors (Basel, Switzerland)
Artificial Intelligence (AI) for human emotion estimation, such as facial emotion estimation, has been actively studied. On the other hand, there has been little research on unconscious phenomena in cognition and psychology (i.e., cognitive biases) c...

An Efficient AP-ANN-Based Multimethod Fusion Model to Detect Stress through EEG Signal Analysis.

Computational intelligence and neuroscience
Stress is a universal emotion that every human experiences daily. Psychologists say stress may lead to heart attack, depression, hypertension, strokes, or even sudden death. Many technical explorations like stress detection through facial expression,...

Proximal Online Gradient Is Optimum for Dynamic Regret: A General Lower Bound.

IEEE transactions on neural networks and learning systems
In online learning, the dynamic regret metric chooses the reference oracle that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is par...

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals.

Sensors (Basel, Switzerland)
The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part of ...

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis.

Computational intelligence and neuroscience
Under the background of the gradual development and popularization of mobile Internet information technology, this paper realizes network public opinion monitoring and emotion analysis based on the deep learning method, aiming at the research needs o...

3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition.

IEEE journal of biomedical and health informatics
Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional ...

Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.

PloS one
We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, "like (positive)" and "dislike (neg...

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.

Sensors (Basel, Switzerland)
Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to improve people's lives. However, most image-based emotion recognition techniques are flawed, as humans c...