AIMC Topic: Neural Networks, Computer

Clear Filters Showing 8841 to 8850 of 31376 articles

Deep multi-task learning for nephropathy diagnosis on immunofluorescence images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As an advanced technique, immunofluorescence (IF) is one of the most widely-used medical image for nephropathy diagnosis, due to its ease of acquisition with low cost. In practice, the clinically collected IF images are comm...

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk.

Journal of environmental management
Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning - an updated version of machine learning techniques - and multi-criteria...

Drop edges and adapt: A fairness enforcing fine-tuning for graph neural networks.

Neural networks : the official journal of the International Neural Network Society
The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Link prediction, in particular, has a substantial social impact. However,...

SWsnn: A Novel Simulator for Spiking Neural Networks.

Journal of computational biology : a journal of computational molecular cell biology
Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other f...

Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence.

PloS one
Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore...

Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms.

PloS one
Accurate prediction of wave overtopping at sea defences remains central to the protection of lives, livelihoods, and infrastructural assets in coastal zones. In addressing the increased risks of rising sea levels and more frequent storm surges, robus...

Deep learning enables the differentiation between early and late stages of hip avascular necrosis.

European radiology
OBJECTIVES: To develop a deep learning methodology that distinguishes early from late stages of avascular necrosis of the hip (AVN) to determine treatment decisions.

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

Magnetic resonance in medicine
PURPOSE: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where oft...

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.

Journal of chemical information and modeling
Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, dete...