AI Medical Compendium Topic:
Time Factors

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Time Series Classification with InceptionFCN.

Sensors (Basel, Switzerland)
Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the la...

Prediction of coronary heart disease based on combined reinforcement multitask progressive time-series networks.

Methods (San Diego, Calif.)
Coronary heart disease is the first killer of human health. At present, the most widely used approach of coronary heart disease diagnosis is coronary angiography, a surgery that could potentially cause some physical damage to the patients, together w...

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Sensors (Basel, Switzerland)
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlyin...

Generation of Time-Series Working Patterns for Manufacturing High-Quality Products through Auxiliary Classifier Generative Adversarial Network.

Sensors (Basel, Switzerland)
Product quality is a major concern in manufacturing. In the metal processing industry, low-quality products must be remanufactured, which requires additional labor, money, and time. Therefore, user-controllable variables for machines and raw material...

Finite-Time Output Synchronization and H Output Synchronization of Coupled Neural Networks With Multiple Output Couplings.

IEEE transactions on cybernetics
This article investigates the finite-time output synchronization and H output synchronization problems for coupled neural networks with multiple output couplings (CNNMOC), respectively. By choosing appropriate state feedback controllers, several fini...

Event-Based Synchronization for Multiple Neural Networks With Time Delay and Switching Disconnected Topology.

IEEE transactions on cybernetics
This article discusses the synchronization problem for a class of multiple delayed neural networks (MDNNs) with a directed switching topology by using an event-triggering strategy. First, a new differential inequality with delay is shown, which is a ...

Quantized Sampled-Data Synchronization of Delayed Reaction-Diffusion Neural Networks Under Spatially Point Measurements.

IEEE transactions on cybernetics
This article considers the synchronization problem of delayed reaction-diffusion neural networks via quantized sampled-data (SD) control under spatially point measurements (SPMs), where distributed and discrete delays are considered. The synchronizat...

A Novel Feature-Engineered-NGBoost Machine-Learning Framework for Fraud Detection in Electric Power Consumption Data.

Sensors (Basel, Switzerland)
This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-p...

Multimode function multistability for Cohen-Grossberg neural networks with mixed time delays.

ISA transactions
In this paper, we are concerned with the multimode function multistability for Cohen-Grossberg neural networks (CGNNs) with mixed time delays. It is introduced the multimode function multistability as well as its specific mathematical expression, whi...