AIMC Topic: Time Factors

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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...

A Convolutional Deep Clustering Framework for Gene Expression Time Series.

IEEE/ACM transactions on computational biology and bioinformatics
The functional or regulatory processes within the cell are explicitly governed by the expression levels of a subset of its genes. Gene expression time series captures activities of individual genes over time and aids revealing underlying cellular dyn...

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations.

Cells
We present a new classification approach for live cells, integrating together the spatial and temporal fluctuation maps and the quantitative optical thickness map of the cell, as acquired by common-path quantitative-phase dynamic imaging and processe...

A systematic literature review of deep learning neural network for time series air quality forecasting.

Environmental science and pollution research international
Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of ai...

Spatial and temporal dynamics of RhoA activities of single breast tumor cells in a 3D environment revealed by a machine learning-assisted FRET technique.

Experimental cell research
One of the hallmarks of cancer cells is their exceptional ability to migrate within the extracellular matrix (ECM) for gaining access to the circulatory system, a critical step of cancer metastasis. RhoA, a small GTPase, is known to be a key molecula...