AIMC Topic: Time Factors

Clear Filters Showing 121 to 130 of 2001 articles

Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.

BMC geriatrics
OBJECTIVES: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

European journal of nuclear medicine and molecular imaging
PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic qu...

Exploring temporal information dynamics in Spiking Neural Networks: Fast Temporal Efficient Training.

Journal of neuroscience methods
BACKGROUND: Spiking Neural Networks (SNNs) hold significant potential in brain simulation and temporal data processing. While recent research has focused on developing neuron models and leveraging temporal dynamics to enhance performance, there is a ...

Neural-network-based practical specified-time resilient formation maneuver control for second-order nonlinear multi-robot systems under FDI attacks.

Neural networks : the official journal of the International Neural Network Society
This paper presents a specified-time resilient formation maneuver control approach for second-order nonlinear multi-robot systems under false data injection (FDI) attacks, incorporating an offline neural network. Building on existing works in integra...

A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the firs...

Self-triggered neural tracking control for discrete-time nonlinear systems via adaptive critic learning.

Neural networks : the official journal of the International Neural Network Society
In this paper, a novel self-triggered optimal tracking control method is developed based on the online action-critic technique for discrete-time nonlinear systems. First, an augmented plant is constructed by integrating the system state with the refe...

A Multi-objective transfer learning framework for time series forecasting with Concept Echo State Networks.

Neural networks : the official journal of the International Neural Network Society
This paper introduces a novel transfer learning framework for time series forecasting that uses Concept Echo State Network (CESN) and a multi-objective optimization strategy. Our approach addresses the challenges of feature extraction and knowledge t...

AI-Driven Analysis of Drug Marketing Efficiency: Unveiling FDA Approval to Market Release Dynamics.

The AAPS journal
This paper explores a novel approach using generative AI to enhance drug marketing strategies in the US pharmaceutical sector. By leveraging an official dataset sourced from the US government, the AI generates Python code to analyze the time interval...

Dynamic graph-based bilateral recurrent imputation network for multivariate time series.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series imputation using graph neural networks (GNNs) has gained significant attention, where the variables and their correlations are depicted as the graph nodes and edges, offering a structured way to understand the intricacies of ...

Learning from leading indicators to predict long-term dynamics of hourly electricity generation from multiple resources.

Neural networks : the official journal of the International Neural Network Society
Electricity is generated through various resources and then flows between regions via a complex system (grid). Imbalances in electricity generation can lead to the waste of renewable energy. As renewable energy is becoming a larger part of the grid, ...