AIMC Topic: Neural Networks, Computer

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Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.

Journal of molecular modeling
CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achie...

Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.

Turkish journal of medical sciences
BACKGROUND/AIM: Tandem mass spectrometry is helpful in diagnosing amino acid metabolism disorders, organic acidemias, and fatty acid oxidation disorders and can provide rapid and accurate diagnosis for inborn errors of metabolism. The aim of this stu...

Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer.

PloS one
PURPOSE: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could ...

Deep demosaicking convolution neural network and quantum wavelet transform-based image denoising.

Network (Bristol, England)
Demosaicking is a popular scientific area that is being explored by a vast number of scientists. Current digital imaging technologies capture colour images with a single monochrome sensor. In addition, the colour images were captured using a sensor c...

Lag projective synchronization of discrete-time fractional-order quaternion-valued neural networks with time delays.

Neural networks : the official journal of the International Neural Network Society
This paper deals with the lag projective synchronization (LPS) problem for a class of discrete-time fractional-order quaternion-valued neural networks(DTFO QVNNs) systems with time delays. Firstly, a DTFOQVNNs system with time delay is constructed. S...

DPGCL: Dual pass filtering based graph contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Graph Contrastive Learning (GCL), which learns node or graph representation from supervision signals derived from the graph data itself, has recently attracted extensive research attention and achieved great success. Remarkably, most of the existing ...

A collaborative neurodynamic approach with two-timescale projection neural networks designed via majorization-minimization for global optimization and distributed global optimization.

Neural networks : the official journal of the International Neural Network Society
In this paper, two two-timescale projection neural networks are proposed based on the majorization-minimization principle for nonconvex optimization and distributed nonconvex optimization. They are proved to be globally convergent to Karush-Kuhn-Tuck...

Deep network embedding with dimension selection.

Neural networks : the official journal of the International Neural Network Society
Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-ba...

An informative dual ForkNet for video anomaly detection.

Neural networks : the official journal of the International Neural Network Society
An autoencoder for video anomaly detection task is a type of algorithm with the primary purpose of learning an "informative" representation of the normal data that can be used for identifying the abnormal data by learning to reconstruct a set of inpu...

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-bas...