WarLens – Transfer Learning for Event Classification in Conflict Zones

Conflict zones present a challenging environment for event classification due to the scarcity of labeled data and the dynamic nature of events. WarLens leverages transfer learning to classify conflict-related events using pre-trained deep learning models fine-tuned with limited domain-specific data. By utilizing models like ResNet50 and MobileNetV2, this study demonstrates the efficacy of transfer learning in achieving high classification accuracy with minimal data, addressing key challenges such as data scarcity and the need for domain adaptation. The research highlights the potential of this approach in enhancing real-time decision-making and improving humanitarian response in conflict-prone areas, while also considering ethical aspects like transparency and bias mitigation in AI decision-making.