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3 days ago · This paper proposes to use an LSTM-based RNN to capture the temporal correlations of the time-varying channel by automatically summarizing the channel ...
6 days ago · In this section, we provide an overview of the literature on causal discovery from time-series, causal representation learning and spatiotemporal causal ...
11 hours ago · Video surveillance faces challenges due to the need for improved anomalous event recognition techniques for human activity recognition.
7 days ago · In this study, we utilized two LSTM models for the primal and dual graphs accordingly to capture the sequential pattern generated across rounds of message- ...
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4 days ago · One type of architecture is to concatenate a two-dimensional (2D) convolutional neural network (CNN), long short-term memory LSTM for capturing spatio-temporal ...
7 days ago · Here, we present an automation paradigm integrating controlling intent into the information processing loop through the spoken instruction-aware flight ...
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5 days ago · There are many applications where ML can be applied, such as energy forecasting, predictive analytics, solar radiation forecasting, wind speed forecasting, ...
1 day ago · This systematic review presents an analysis of CED methods for video streams described in publications from 2012 to 2024, focusing on their effectiveness in ...
3 days ago · Because target tracking corresponds to a time series forecasting problem, autoregressive and recurrent networks are among the best learning techniques due to ...
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4 days ago · Therefore, the sensor data exhibits spatio-temporal correlations and high-dimensional characteristics, such as bearing wear data, motor condition data, and air ...
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