AI Medical Compendium Topic:
Time Factors

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Improved Stability Criteria for Discrete-Time Delayed Neural Networks via Novel Lyapunov-Krasovskii Functionals.

IEEE transactions on cybernetics
This article investigates the stability problem for discrete-time neural networks with a time-varying delay by focusing on developing new Lyapunov-Krasovskii (L-K) functionals. A novel L-K functional is deliberately tailored from two aspects: 1) the ...

Stabilization via Event-Triggered Impulsive Control With Constraints for Switched Stochastic Systems.

IEEE transactions on cybernetics
This article studies the event-triggered impulsive control (ETIC) with constraints for the stabilization of switched stochastic systems (SSSs). An ETIC scheme with constraints is proposed for SSS by designing two levels of events via three indices: 1...

Delay Compensation-Based State Estimation for Time-Varying Complex Networks With Incomplete Observations and Dynamical Bias.

IEEE transactions on cybernetics
In this article, a delay-compensation-based state estimation (DCBSE) method is given for a class of discrete time-varying complex networks (DTVCNs) subject to network-induced incomplete observations (NIIOs) and dynamical bias. The NIIOs include the c...

Image-based time series forecasting: A deep convolutional neural network approach.

Neural networks : the official journal of the International Neural Network Society
Inspired by the successful use of deep learning in computer vision, in this paper we introduce ForCNN, a novel deep learning method for univariate time series forecasting that mixes convolutional and dense layers in a single neural network. Instead o...

DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction.

Neural networks : the official journal of the International Neural Network Society
Time series forecasting models that use the past information of exogenous or endogenous sequences to forecast future series play an important role in the real world because most real-world time series datasets are rich in time-dependent information. ...

Machine learning for yield prediction for chemical reactions using in situ sensors.

Journal of molecular graphics & modelling
Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reacti...

Turnaround time prediction for clinical chemistry samples using machine learning.

Clinical chemistry and laboratory medicine
OBJECTIVES: Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare...

An efficient and low complex model for optimal RBM features with weighted score-based ensemble multi-disease prediction.

Computer methods in biomechanics and biomedical engineering
Multi-disease prediction is regarded as the capacity to simultaneously identify various diseases that are expected to be affected an individual at a certain period. These multiple diseases are seemed to be at various progression levels and need to be...

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.

Methods of information in medicine
BACKGROUND: Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputa...

Practical synchronization of neural networks with delayed impulses and external disturbance via hybrid control.

Neural networks : the official journal of the International Neural Network Society
This paper studies the problem of practical synchronization for delayed neural networks via hybrid-driven impulsive control in which delayed impulses and external disturbance are taken into account. Firstly, a switching method which establishes the r...