AIMC Topic: Neural Networks, Computer

Clear Filters Showing 6501 to 6510 of 31376 articles

Unsupervised domain adaptation with weak source domain labels via bidirectional subdomain alignment.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation (UDA) enables knowledge transfer from a labeled source domain to an unlabeled target domain. However, UDA performance often relies heavily on the accuracy of source domain labels, which are frequently noisy or missing i...

KEMoS: A knowledge-enhanced multi-modal summarizing framework for Chinese online meetings.

Neural networks : the official journal of the International Neural Network Society
The demand for "online meetings" and "collaborative office work" keeps surging recently, producing an abundant amount of relevant data. How to provide participants with accurate and fast summarizing service has attracted extensive attention. Existing...

Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors.

Environmental research
To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-b...

Graph convolutional network with attention mechanism improve major depressive depression diagnosis based on plasma biomarkers and neuroimaging data.

Journal of affective disorders
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presenta...

Optimizing large language models in digestive disease: strategies and challenges to improve clinical outcomes.

Liver international : official journal of the International Association for the Study of the Liver
Large Language Models (LLMs) are transformer-based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts an...

Fully Automatic Quantitative Measurement of Equilibrium Radionuclide Angiocardiography Using a Convolutional Neural Network.

Clinical nuclear medicine
PURPOSE: The aim of this study was to generate deep learning-based regions of interest (ROIs) from equilibrium radionuclide angiography datasets for left ventricular ejection fraction (LVEF) measurement.

HD-Former: A hierarchical dependency Transformer for medical image segmentation.

Computers in biology and medicine
Medical image segmentation is a compelling fundamental problem and an important auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a valuable tool for addressing the limitations of convolutional neural networks b...

Seizure Detection Based on Lightweight Inverted Residual Attention Network.

International journal of neural systems
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detectio...

Enhancement of ANN-based wind power forecasting by modification of surface roughness parameterization over complex terrain.

Journal of environmental management
Wind energy plays an important role in the sustainable energy transition towards a low-carbon society. Proper assessment of wind energy resources and accurate wind energy prediction are essential prerequisites for balancing electricity supply and dem...

Blockchain and IoT integration for secure short-term and long-term air quality monitoring system using optimized neural network.

Environmental science and pollution research international
Accurate air pollution prediction is vital for residents' well-being. This research introduces a secure air quality monitoring system using neural networks and blockchain for robust analysis, precise predictions, and early pollution detection. Blockc...