Abstract: Due to the intrinsic complexity of time series forecasting within power systems, artificial intelligence has emerged as a promising pathway for predictive analytics. Although time series ...
Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series ...
Firefighters battled Thursday to control a series of major fires in the Los Angeles area that have killed seven people, burned at least 10,000 structures from the Pacific Coast to Pasadena and sent ...
Abstract: As the Internet of Things system becomes more popular and ubiquitous, it has also gradually entered the consumer electronics field. For example, smart home systems have numerous sensors that ...
This toolbox enables hyperparameter optimization for autoencoders using a genetic algorithm. This framework extends the framework "Generic Deep Autoencoder for Time-Series" by providing an algorithm ...
This toolbox enables the simple implementation of different deep autoencoder. The primary focus is on multi-channel time-series analysis. Each autoencoder consists of two, possibly deep, neural ...
Objectives To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data. Design A single-centre, retrospective cohort ...
Space generally overshadows time in the construction of theories in cognitive neuroscience. In this paper, we pivot from the spatial axes to the temporal, analyzing fMRI image series to reveal ...
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