Shaymaa E Sorour

Abstract This study investigates multi-class modeling of VIIRS satellite fire detection confidence levels (low, medium, high) using twelve years (2012–2023) of thermal, radiative, spatial, and temporal observations over Saudi Arabia. Detection confidence is formulated as a reliability-oriented structured learning problem.

Abstract One of the problematic issues in textile production is quality control in fancy yarn because of the complicated structure and aesthetic needs of these goods. This paper presents a novel integrated system that combines artificial intelligence and computer vision algorithms for quality assurance of fancy yarns. The proposed system has been designed, implemented as a laboratory prototype, and experimentally validated under controlled conditions.

Abstract The growing incidence of agricultural fires in arid and semi-arid regions necessitates the development of advanced, scalable systems for early detection. This study presents FlameGuard, a geospatial AI framework developed to classify fire confidence levels using thermal satellite imagery-based data from the FIRMS dataset and multi-model predictive architectures.