AIMC Topic: Neural Networks, Computer

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A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks.

Scientific reports
The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter es...

NAH-GNN: A graph-based framework for multi-behavior and high-hop interaction recommendation.

PloS one
With the growing demand for personalized marketing, recommender systems have become essential tools to help users quickly discover products or content that match their interests. However, traditional recommendation methods face significant limitation...

Prediction of stress-strain behavior of rock materials under biaxial compression using a deep learning approach.

PloS one
Deep learning has significantly advanced in predicting stress-strain curves. However, due to the complex mechanical properties of rock materials, existing deep learning methods have the problem of insufficient accuracy in predicting the stress-strain...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ign...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an ...

Advanced data-driven interpretable analysis for predicting resistant starch content in rice using NIR spectroscopy.

Food chemistry
Resistant starch (RS) is a vital dietary component with notable health benefits, but tradition quantification methods are labor-intensive, costly, and unsuitable for large-scale applications. This study introduced an innovative data-driven framework ...

S-Net: Learning spectral-spatio self-similarity for hyperspectral image super-resolution.

Neural networks : the official journal of the International Neural Network Society
As an economically feasible approach for hyperspectral image (HSI) super-resolution, fusing HSI with multispectral image (MSI) utilizes the complementary nature of cross-modality information. Given the common presence of repetitive textures and struc...

Self-supervised spatial-temporal contrastive network for EEG-based brain network classification.

Neural networks : the official journal of the International Neural Network Society
Electroencephalogram (EEG)-based brain network analysis has shown promise in brain disease research by revealing the complex connectivity among brain regions. However, existing methods struggle to fully utilize the large amounts of unlabeled data to ...

MSCViT: A small-size ViT architecture with multi-scale self-attention mechanism for tiny datasets.

Neural networks : the official journal of the International Neural Network Society
Vision Transformer (ViT) has demonstrated significant potential in various vision tasks due to its strong ability in modeling long-range dependencies. However, such success is largely fueled by training on massive samples. In real applications, the l...

Global-Local Feature Fusion Network Based on Nonlinear Spiking Neural Convolutional Model for MRI Brain Tumor Segmentation.

International journal of neural systems
Due to the differences in size, shape, and location of brain tumors, brain tumor segmentation differs greatly from that of other organs. The purpose of brain tumor segmentation is to accurately locate and segment tumors from MRI images to assist doct...