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An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.

Computers in biology and medicine
This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion classification is one of the most basic approaches for improving the quality of emotion recognition syst...

Artificial Intelligence-based System for Detecting Attention Levels in Students.

Journal of visualized experiments : JoVE
The attention level of students in a classroom can be improved through the use of Artificial Intelligence (AI) techniques. By automatically identifying the attention level, teachers can employ strategies to regain students' focus. This can be achieve...

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Sensors (Basel, Switzerland)
Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an...

Corruption depth: Analysis of DNN depth for misclassification.

Neural networks : the official journal of the International Neural Network Society
Many large and complex deep neural networks have been shown to provide higher performance on various computer vision tasks. However, very little is known about the relationship between the complexity of the input data along with the type of noise and...

A computational approach to investigating facial attractiveness factors using geometric morphometric analysis and deep learning.

Scientific reports
Numerous studies discuss the features that constitute facial attractiveness. In recent years, computational research has received attention because it can examine facial features without relying on prior research hypotheses. This approach uses many f...

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

Sensors (Basel, Switzerland)
Predicting pilots' mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which a...

MMBERT: a unified framework for biomedical named entity recognition.

Medical & biological engineering & computing
Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the comp...

Tell me your position: Distantly supervised biomedical entity relation extraction using entity position marker.

Neural networks : the official journal of the International Neural Network Society
A significant amount of textual data has been produced in the biomedical area recently as a result of the advancement of biomedical technologies. Large-scale biomedical data can be automatically obtained with the help of distant supervision. However,...

Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography.

Neural networks : the official journal of the International Neural Network Society
Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved...

Cultural differences in joint attention and engagement in mutual gaze with a robot face.

Scientific reports
Joint attention is a pivotal mechanism underlying human ability to interact with one another. The fundamental nature of joint attention in the context of social cognition has led researchers to develop tasks that address this mechanism and operationa...