AIMC Topic: Humans

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A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame.

IEEE transactions on neural networks and learning systems
To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement...

GREnet: Gradually REcurrent Network With Curriculum Learning for 2-D Medical Image Segmentation.

IEEE transactions on neural networks and learning systems
Medical image segmentation is a vital stage in medical image analysis. Numerous deep-learning methods are booming to improve the performance of 2-D medical image segmentation, owing to the fast growth of the convolutional neural network. Generally, t...

Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human-Robot Interaction.

IEEE transactions on neural networks and learning systems
A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracte...

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

IEEE transactions on neural networks and learning systems
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...

Versatile Graph Neural Networks Toward Intuitive Human Activity Understanding.

IEEE transactions on neural networks and learning systems
Benefiting from the advanced human visual system, humans naturally classify activities and predict motions in a short time. However, most existing computer vision studies consider those two tasks separately, resulting in an insufficient understanding...

Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.

IEEE transactions on neural networks and learning systems
Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity patte...

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.

Theranostics
The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensit...

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study.

Frontiers in public health
BACKGROUND: Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigor...

COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.

PloS one
During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases...

Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

PloS one
This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), U...