S. Abdullahi

An explainable CNN–BiLSTM–Random Forest framework for epidemic warning-signal and disease-status classification

This study evaluates an explainable ensemble in which Convolutional Neural Network and Bidirectional Long Short-Term Memory feature extractors feed a Random Forest classifier, applied under one architectural template to two independent tasks: labelling Ebola-related tweets as symptom-bearing warning signals and labelli...