BanglaMM-Disaster: A Multimodal Transformer-Based Deep Learning Framework for Multiclass Disaster Classification in Bangla
PositiveArtificial Intelligence
- The BanglaMM-Disaster framework has been introduced as a multimodal deep learning solution for classifying disasters in Bangladesh, utilizing both textual and visual data from social media. This innovative model processes a dataset of 5,037 Bangla posts, achieving an accuracy of 83.76%, which is a significant improvement over previous models that relied solely on text or images.
- This development is crucial for enhancing disaster response systems in Bangladesh, a country frequently affected by natural disasters. By providing real-time monitoring and classification capabilities, the framework aims to improve the efficiency and effectiveness of emergency responses, potentially saving lives and resources.
- The integration of advanced AI techniques, such as transformer-based encoders and CNN backbones, reflects a growing trend in disaster management towards leveraging multimodal data. This approach not only enhances classification accuracy but also addresses the challenges of misclassification, which is vital in high-stakes scenarios like disaster response.
— via World Pulse Now AI Editorial System
