Ensemble Deep Learning

Ensemble deep learning combines multiple deep neural networks to improve the accuracy, robustness, and generalization of predictions across diverse applications. Current research focuses on applying ensemble methods to various tasks, including medical image analysis (e.g., brain tumor classification, sleep stage identification, diabetic retinopathy detection), natural language processing (e.g., sentiment analysis, machine translation), and signal processing (e.g., speech emotion recognition, traffic accident detection). This approach addresses challenges like class imbalance and data heterogeneity, leading to improved performance compared to single models, and holds significant promise for advancing numerous fields by providing more reliable and accurate predictions.