AIMC Topic: Neural Networks, Computer

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Temporal and spatial feature extraction using graph neural networks for multi-point water quality prediction in river network areas.

Water research
Deep learning methods have demonstrated strong capabilities in capturing nonlinear relationships for water quality prediction, yet existing studies predominantly focus on individual monitoring sites while neglecting pollutant spatial dynamics. To add...

CCA: Contrastive cluster assignment for supervised and semi-supervised medical image segmentation.

Neural networks : the official journal of the International Neural Network Society
Transformers have shown great potential in vision tasks such as semantic segmentation. However, most of the existing transformer-based segmentation models neglect the cross-attention between pixel features and class features which impedes the applica...

SuperM2M: Supervised and mixture-to-mixture co-learning for speech enhancement and noise-robust ASR.

Neural networks : the official journal of the International Neural Network Society
The current dominant approach for neural speech enhancement is based on supervised learning by using simulated training data. The trained models, however, often exhibit limited generalizability to real-recorded data. To address this, this paper inves...

Modelling fourth-order hyperelasticity in soft solids using physics informed neural networks without labelled data.

Brain research bulletin
Mild traumatic brain injury can result from shear shock wave formation in the brain in the event of a head impact like in contact sports, road traffic accidents, etc. These highly nonlinear deformations are modelled by a fourth-order Landau hyperelas...

Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks.

Computers in biology and medicine
Breast cancer is a leading cause of mortality worldwide. Screening therefore remains the best defense against this disease, highlighting the need for accurate and efficient diagnostic methods. Previous authors addressed this issue by implementing dig...

Role of physics-informed constraints in real-time estimation of 3D vascular fluid dynamics using multi-case neural network.

Computers in biology and medicine
Numerical simulations of fluid dynamics in tube-like structures are important to biomedical research to model flow in blood vessels and airways. It is further useful to some clinical applications, such as predicting arterial fractional flow reserves,...

GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.

Pharmaceutical research
PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syn...

Neural Network With Attention Mechanism for Abnormality Detection and Localization in Diffusive Molecular Communication.

IEEE transactions on nanobioscience
Diffusive molecular communication (DMC) is an emerging paradigm in nanotechnology, which provides biocompatibility and nanoscale communication for many promising applications, such as targeted drug delivery, environmental monitoring, etc. However, de...

Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group A .

Journal of microbiology and biotechnology
Group A (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these finding...

Deep learning based quantitative cervical vertebral maturation analysis.

Head & face medicine
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...