A machine learning-based prediction model for preterm birth in rural areas

Abstract

Wildfires are a worldwide natural disaster causing important economic damages and loss of lives . Early detection and prediction of fire spread can help reduce affected areas and improve firefighting. Unmanned Aerial Vehicles were employed to tackle this problem due to their HIGH flexibility, their low-cost, and their ability to cover wide areas during the day or night. However , they are still limited by challenging problems such as small fire size, background complexity, and image degradation .To deal with the limitations, we adapted and optimized Deep Learning methods to detect wildfire at an early stage. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfire using aerial images.