CNN BiLSTM

CNN-BiLSTM models combine convolutional neural networks (CNNs) for spatial feature extraction with bidirectional long short-term memory networks (BiLSTMs) to capture temporal dependencies in sequential data, primarily addressing tasks involving both spatial and temporal patterns. Current research focuses on applying this architecture to diverse problems, including sentiment analysis, emotion classification, and time-series prediction, often incorporating enhancements like attention mechanisms and feature pyramids to improve performance. The versatility of CNN-BiLSTMs makes them valuable tools across various fields, from improving tornado prediction accuracy and mental health monitoring through social media analysis to enhancing handwriting recognition and optical transmission systems.