Results 161 to 170 of about 13,592 (226)
Subject-Independent Depression Recognition from EEG Using an Improved Bidirectional LSTM with Dynamic Vector Routing. [PDF]
Ji Z, Liu K, Ma W, Ning X, Gao Y.
europepmc +1 more source
An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. [PDF]
Pustokhin DA +6 more
europepmc +1 more source
Abstract Precipitation nowcasting refers to the high‐resolution forecasting of rainfall and hydrometeors within 0–6 hr according to the official definition of the World Meteorological Organization, which has relied on numerical models for decades. Recently, artificial intelligence (AI) has shown promise in addressing precipitation nowcasting.
Nan Yang +3 more
wiley +1 more source
Advancing epileptic seizure recognition through bidirectional LSTM networks. [PDF]
Al-Marzouki S.
europepmc +1 more source
Abstract Achieving both accuracy and interpretability in deep learning models for geochemical anomaly recognition constitutes a significant challenge. To overcome this challenge, this study developed a novel interpretable dual‐branch network combining a spectral attention bidirectional RNN (BiRNN) branch and a spatial attention CNN branch guided with ...
Yihui Xiong +4 more
wiley +1 more source
Application of stacked bidirectional LSTM neural networks in reservoir porosity prediction. [PDF]
Zhang P, Hu S, Xiao Y, Chen P, Sun D.
europepmc +1 more source
Abstract Precise thermospheric density forecasting is critical for mitigating satellite drag in Low Earth Orbit (LEO), but traditional empirical models such as MSIS and JB2008 can fail during geomagnetic storms. To evaluate whether AI can better capture this transient behavior, the 2025 MIT ARCLab Prize for AI Innovation in Space asked participants to ...
Sergio Sanchez‐Hurtado +18 more
wiley +1 more source
Working mode detection method based on bidirectional LSTM for pipe jacking inertial automatic guidance system. [PDF]
Zu Y +5 more
europepmc +1 more source
Abstract This study presents a novel multitask deep learning framework that integrates models designed for simultaneous, multi‐horizon forecasting of Hp60 and Disturbance storm time (Dst) indices with lead times up to 3 hr. Using a Long Short‐Term Memory architecture, the models capture both mid‐latitude variability and the ring current dynamics. Input
Joseph Kagotho Muriithi +4 more
wiley +1 more source
BLSAM-TIP: Improved and robust identification of tyrosinase inhibitory peptides by integrating bidirectional LSTM with self-attention mechanism. [PDF]
Ahmed S +5 more
europepmc +1 more source

