Deep learning

Deep learning framework with quantum-inspired feature fusion achieves 99.33% accuracy for automated enamel caries classification in intraoral photographs with visual explainability.

Deep reinforcement learning framework for adaptive task scheduling in edge-cloud computing with improved SLA compliance, reduced operational costs, and lower task rejection rates.

Emotion-Conditioned Deep Reinforcement Learning framework for adaptive music generation. Achieves 98% emotion mapping accuracy with 280ms real-time responsiveness, enabling dynamic musical.

Hybrid explainable AI and Bayesian deep learning system predicts dengue outbreaks at district level in Bangladesh using climate, socioeconomic, and healthcare data from 2017-2024.

Deep learning framework quantifies craniofacial growth patterns and sexual dimorphism from cephalometric radiographs using automated feature extraction and saliency mapping without manual annotation.

Review synthesizes big data and machine learning integration in U.S. infectious disease surveillance, examining data sources and computational methods for detection, forecasting, and control.

Deep learning vision-language model for diagnosing pediatric dental diseases in panoramic radiographs, combining visual and textual information with 90% accuracy for caries and periapical.

Deep learning model using ResNet-50 classifies fibro-osseous jaw lesions from histology images with 86% accuracy, outperforming pathologists in differentiating fibrous dysplasia, cemento-ossifying.