AIMC Topic: Time Factors

Clear Filters Showing 421 to 430 of 2001 articles

GRAND: GAN-based software runtime anomaly detection method using trace information.

Neural networks : the official journal of the International Neural Network Society
Software runtime anomaly detection can detect manifestations (known as anomalies) caused by faults in complex systems before they lead to failure. Whereas most existing methods use external performance indicators, this study uses internal execution t...

Reservoir computing models based on spiking neural P systems for time series classification.

Neural networks : the official journal of the International Neural Network Society
Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP systems can show rich nonlinear dynamics. Reservoir computing (RC) is a novel rec...

Efficacy and Safety of Chinese Herbal Medicine in Patients with Acute Intracerebral Hemorrhage: Protocol for a Randomized Placebo-Controlled Double-Blinded Multicenter Trial.

Cerebrovascular diseases (Basel, Switzerland)
INTRODUCTION: The popular traditional Chinese medicine (TCM) compound FYTF-919 (Zhong Feng Xing Nao prescription) may improve outcome from acute intracerebral hemorrhage (ICH) through effects on brain edema, hematoma absorption, and the immune system...

Learning pharmacometric covariate model structures with symbolic regression networks.

Journal of pharmacokinetics and pharmacodynamics
Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for ...

Saturation function-based continuous control on fixed-time synchronization of competitive neural networks.

Neural networks : the official journal of the International Neural Network Society
Currently, through proposing discontinuous control strategies with the signum function and discussing separately short-term memory (STM) and long-term memory (LTM) of competitive artificial neural networks (ANNs), the fixed-time (FXT) synchronization...

Ultra-fast deep-learned CNS tumour classification during surgery.

Nature
Central nervous system tumours represent one of the most lethal cancer types, particularly among children. Primary treatment includes neurosurgical resection of the tumour, in which a delicate balance must be struck between maximizing the extent of r...

Detecting changes in the performance of a clinical machine learning tool over time.

EBioMedicine
BACKGROUND: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a m...

STTRE: A Spatio-Temporal Transformer with Relative Embeddings for multivariate time series forecasting.

Neural networks : the official journal of the International Neural Network Society
The prevalence of multivariate time series data across several disciplines fosters a demand and, subsequently, significant growth in the research and advancement of multivariate time series analysis. Drawing inspiration from a popular natural languag...

Impact of heart failure on reoperation in adult congenital heart disease: An innovative machine learning model.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The study objectives were to evaluate the association between preoperative heart failure and reoperative cardiac surgical outcomes in adult congenital heart disease and to develop a risk model for postoperative morbidity/mortality.

Unsupervised anomaly detection by densely contrastive learning for time series data.

Neural networks : the official journal of the International Neural Network Society
Time series data continuously collected by different sensors play an essential role in monitoring and predicting events in many real-world applications, and anomaly detection for time series has received increasing attention during the past decades. ...