Attention Based Bidirectional

Attention-based bidirectional recurrent neural networks (RNNs), particularly BiLSTMs and BiGRUs, are increasingly used to process sequential data where contextual information is crucial. Current research focuses on applying these architectures to diverse tasks, including identifying citation needs in scientific papers, detecting opioid users in social media posts, and classifying ECG heartbeats and human interactions from sensor data. These models' ability to capture both forward and backward dependencies, combined with attention mechanisms highlighting important features, leads to improved accuracy and interpretability in various domains, offering valuable tools for automated analysis and decision support.