AIMC Topic: Neural Networks, Computer

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Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.

PloS one
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning,...

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

PloS one
Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilities, a precise, rapid, and affordable method for detecting ammonia concentrations is essential. We d...

Machine Learning Algorithms for Prediction of Ambulation and Wheelchair Transfer Ability in Spina Bifida.

Archives of physical medicine and rehabilitation
OBJECTIVE: To determine which statistical techniques enhance our ability to predict ambulation and transfer ability in people with spina bifida (SB).

Machine learning approaches for predicting craniofacial anomalies with graph neural networks.

Computational biology and chemistry
This study explores the use of machine learning algorithms, including traditional approaches and graph neural networks (GNNs), to predict certain diseases by analyzing protein-protein interactions. Protein-protein interactions (PPIs) are complex, mul...

MNet: A multi-scale network for visible watermark removal.

Neural networks : the official journal of the International Neural Network Society
Superimposing visible watermarks on images is an efficient way to indicate ownership and prevent potential unauthorized use. Visible watermark removal technology is receiving increasing attention from researchers due to its ability to enhance the rob...

Improving forward compatibility in class incremental learning by increasing representation rank and feature richness.

Neural networks : the official journal of the International Neural Network Society
Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining knowledge obtained from prior tasks. Although previous studies have pr...

PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction.

Neural networks : the official journal of the International Neural Network Society
Traffic flow prediction is the foundation of intelligent traffic management systems. Current methods prioritize the development of intricate models to capture spatio-temporal correlations, yet they often neglect the exploitation of latent features wi...

Intra- and inter-channel deep convolutional neural network with dynamic label smoothing for multichannel biosignal analysis.

Neural networks : the official journal of the International Neural Network Society
Efficient processing of multichannel biosignals has significant application values in the fields of healthcare and human-machine interaction. Although previous research has achieved high recognition performance with deep convolutional neural networks...

Cognitive process and information processing model based on deep learning algorithms.

Neural networks : the official journal of the International Neural Network Society
According to the developmental process of infants, cognitive abilities are divided into four stages: the Exploration Stage (ES), the Mapping Stage (MS), the Phenomena-causality Stage (PCS), and the Essence-causality Stage (ECS). The MS is a training ...

Self-supervised neural network for Patlak-based parametric imaging in dynamic [F]FDG total-body PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: The objective of this study is to generate reliable K parametric images from a shortened [F]FDG total-body PET for clinical applications using a self-supervised neural network algorithm.