AIMC Topic: Neural Networks, Computer

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Dual-view graph-of-graph representation learning with graph Transformer for graph-level anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Graph-Level Anomaly Detection (GLAD) endeavors to pinpoint a small subset of anomalous graphs that deviate from the normal data distribution within a given set of graph data. Existing GLAD methods typically rely on Graph Neural Networks (GNNs) to ext...

Quantum federated learning with pole-angle quantum local training and trainable measurement.

Neural networks : the official journal of the International Neural Network Society
Recently, quantum federated learning (QFL) has received significant attention as an innovative paradigm. QFL has remarkable features by employing quantum neural networks (QNNs) instead of conventional neural networks owing to quantum supremacy. In or...

Anxiety disorder identification with biomarker detection through subspace-enhanced hypergraph neural network.

Neural networks : the official journal of the International Neural Network Society
In this study, we propose a subspace-enhanced hypergraph neural network (seHGNN) for classifying anxiety disorders (AD), which are prevalent mental illnesses that affect a significant portion of the global population. Our seHGNN model utilizes a lear...

Image debanding using cross-scale invertible networks with banded deformable convolutions.

Neural networks : the official journal of the International Neural Network Society
Banding artifacts in images stem from limitations in color bit depth, image compression, or over-editing, significantly degrades image quality, especially in regions with smooth gradients. Image debanding is about eliminating these artifacts while pr...

Hyperspectral anomaly detection with self-supervised anomaly prior.

Neural networks : the official journal of the International Neural Network Society
Hyperspectral anomaly detection (HAD) can identify and locate the targets without any known information and is widely applied in Earth observation and military fields. The majority of existing HAD methods use the low-rank representation (LRR) model t...

Spatio-temporal prediction of groundwater vulnerability based on CNN-LSTM model with self-attention mechanism: A case study in Hetao Plain, northern China.

Journal of environmental sciences (China)
Located in northern China, the Hetao Plain is an important agro-economic zone and population centre. The deterioration of local groundwater quality has had a serious impact on human health and economic development. Nowadays, the groundwater vulnerabi...

Machine learning-assisted aroma profile prediction in Jiang-flavor baijiu.

Food chemistry
The complex flavor of Jiang-flavor Baijiu (JFB) arises from the interaction of hundreds of compounds at both physicochemical and sensory levels, making accurate perception challenging. Modern machine learning techniques offer precise and scientific a...

A general deep learning model for predicting and classifying pea protein content via visible and near-infrared spectroscopy.

Food chemistry
Rapid and accurate detection of pea protein content is crucial for breeding and ensuring food quality. This study introduces the PeaNet model, which employs an improved convolutional neural network architecture to predict and classify pea protein con...

Extracting True Virus SERS Spectra and Augmenting Data for Improved Virus Classification and Quantification.

ACS sensors
Surface-enhanced Raman spectroscopy (SERS) is a transformative tool for infectious disease diagnostics, offering rapid and sensitive species identification. However, background spectra in biological samples complicate analyte peak detection, increase...