AIMC Topic: Neural Networks, Computer

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Harnessing collective structure knowledge in data augmentation for graph neural networks.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are a...

Multi-view scene matching with relation aware feature perception.

Neural networks : the official journal of the International Neural Network Society
For scene matching, the extraction of metric features is a challenging task in the face of multi-source and multi-view scenes. Aiming at the requirements of multi-source and multi-view scene matching, a siamese network model for Spatial Relation Awar...

PathMLP: Smooth path towards high-order homophily.

Neural networks : the official journal of the International Neural Network Society
Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, fr...

The Artificial Neural Twin - Process optimization and continual learning in distributed process chains.

Neural networks : the official journal of the International Neural Network Society
Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasi...

StochCA: A novel approach for exploiting pretrained models with cross-attention.

Neural networks : the official journal of the International Neural Network Society
Utilizing large-scale pretrained models is a well-known strategy to enhance performance on various target tasks. It is typically achieved through fine-tuning pretrained models on target tasks. However, naï ve fine-tuning may not fully leverage knowle...

Asynchronous Numerical Spiking Neural Membrane Systems with Local Synchronization.

International journal of neural systems
Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information...

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

International journal of neural systems
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalizatio...

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes.

Computer methods and programs in biomedicine
OBJECTIVE: In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling p...

Research on low-power driving fatigue monitoring method based on spiking neural network.

Experimental brain research
Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detect...

A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents.

Sensors (Basel, Switzerland)
Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention techn...