Transformer-based detector spots camouflaged soybean pests more accurately
An enhanced RT-DETR-ResNet18 framework raises detection accuracy for camouflaged soybean pests, reaching 77.4% mAP@0.5 across 12 pest categories.
An enhanced RT-DETR-ResNet18 framework raises detection accuracy for camouflaged soybean pests, reaching 77.4% mAP@0.5 across 12 pest categories.
Researchers trained a reinforcement learning controller entirely on a neural-operator digital twin and deployed it on the MuVacAS vacuum prototype ...
A diffusion-based text-to-speech model trained on eight dysarthric TORGO speakers generated 6,120 synthetic UA-Speech utterances that improved speaker-independent intelligibility classification ...
A systematic review of 120 papers finds deep learning DDoS detectors excel in experiments but struggle in real-world cloud environments, ...
A new EEG study combined entropy-optimized symbolic signal analysis with a bidirectional LSTM network to distinguish children with ADHD from ...
A simulator-based hybrid quantum-classical framework reported 98.5% accuracy in predicting cross-border e-commerce marketing effectiveness, outperforming classical baselines on synthetic transaction ...
A preliminary case series found that an AI facial analysis application agreed with a single dermatologist on 83 percent of ...
A systematic review of 78 papers examines how machine learning methods support demand forecasting and inventory decision-making.
A new deep learning framework called RCNNricePPI predicts rice protein-protein interactions from amino acid sequences, achieving an accuracy of 0.8996 ...
A systematic review of 306 studies finds most machine learning crash severity models ignore class imbalance and a large share ...
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© 2025 Scienmag - Science Magazine