AIMC Topic: Time Factors

Clear Filters Showing 271 to 280 of 2001 articles

Impact of artificial intelligence assisted compressed sensing technique on scan time and image quality in musculoskeletal MRI - A systematic review.

Radiography (London, England : 1995)
INTRODUCTION: Magnetic Resonance Imaging (MRI) has revolutionized the diagnosis and treatment of musculoskeletal disorders. Parallel imaging (PI) and compressed sensing (CS) techniques reduce scan time, but higher acceleration factors decrease image ...

Relaxed stability criteria of delayed neural networks using delay-parameters-dependent slack matrices.

Neural networks : the official journal of the International Neural Network Society
This note aims to reduce the conservatism of stability criteria for neural networks with time-varying delay. To this goal, on the one hand, we construct an augmented Lyapunov-Krasovskii functional (LKF), incorporating some delay-product terms that ca...

Time-series domain adaptation via sparse associative structure alignment: Learning invariance and variance.

Neural networks : the official journal of the International Neural Network Society
Domain adaptation on time-series data, which is often encountered in the field of industry, like anomaly detection and sensor data forecasting, but received limited attention in academia, is an important but challenging task in real-world scenarios. ...

Deep learning model for intravascular ultrasound image segmentation with temporal consistency.

The international journal of cardiovascular imaging
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elast...

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

Journal of the American Heart Association
BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke o...

Multi-source fully test-time adaptation.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks have significantly advanced various fields. However, these models often encounter difficulties in achieving effective generalization when the distribution of test samples varies from that of the training samples. Recently, some f...

An optimal fast fractal method for breast masses diagnosis using machine learning.

Medical engineering & physics
This article introduces a fast fractal method for classifying breast cancerous lesions in mammography. While fractal methods are valuable for extracting information, they often come with a high computational load and time consumption. This paper demo...

Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.

BMC ophthalmology
BACKGROUND: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the...

Learning the feature distribution similarities for online time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Identifying anomalies in multi-dimensional sequential data is crucial for ensuring optimal performance across various domains and in large-scale systems. Traditional contrastive methods utilize feature similarity between different features extracted ...