AIMC Topic: Neural Networks, Computer

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Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.

JMIR aging
BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily re...

Basic research for identification and classification of organophosphorus pesticides in water based on ultraviolet-visible spectroscopy information.

Environmental science and pollution research international
In this study, the goal was to develop a method for detecting and classifying organophosphorus pesticides (OPPs) in bodies of water. Sixty-five samples with different concentrations were prepared for each of the organophosphorus pesticides, namely ch...

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...

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.

Interdisciplinary sciences, computational life sciences
The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs ...