AIMC Topic: Neural Networks, Computer

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Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods.

Tropical animal health and production
This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were...

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Journal of computational neuroscience
Spiking neural networks (SNNs), as the third generation of neural networks, are based on biological models of human brain neurons. In this work, a shallow SNN plays the role of an explicit image decoder in the image classification. An LSTM-based EEG ...

Statistically unbiased prediction enables accurate denoising of voltage imaging data.

Nature methods
Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pix...

DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network.

BMC bioinformatics
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations su...

FDNet: An end-to-end fusion decomposition network for infrared and visible images.

PloS one
Infrared and visible image fusion can generate a fusion image with clear texture and prominent goals under extreme conditions. This capability is important for all-day climate detection and other tasks. However, most existing fusion methods for extra...

Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

PloS one
Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mos...

A deep sift convolutional neural networks for total brain volume estimation from 3D ultrasound images.

Computer methods and programs in biomedicine
Preterm infants are a highly vulnerable population. The total brain volume (TBV) of these infants can be accurately estimated by brain ultrasound (US) imaging which enables a longitudinal study of early brain growth during Neonatal Intensive Care (NI...

A biologically inspired architecture with switching units can learn to generalize across backgrounds.

Neural networks : the official journal of the International Neural Network Society
Humans and other animals navigate different environments effortlessly, their brains rapidly and accurately generalizing across contexts. Despite recent progress in deep learning, this flexibility remains a challenge for many artificial systems. Here,...

CEGAT: A CNN and enhanced-GAT based on key sample selection strategy for hyperspectral image classification.

Neural networks : the official journal of the International Neural Network Society
In recent years, the application of convolutional neural networks (CNNs) and graph convolutional networks (GCNs) in hyperspectral image classification (HSIC) has achieved remarkable results. However, the limited label samples are still a major challe...

Mean-field neural networks: Learning mappings on Wasserstein space.

Neural networks : the official journal of the International Neural Network Society
We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/control problems. Two classes of neural networks based on bin density a...