Deep Learning Model Reaches 98.39% Accuracy in Cotton Leaf Disease Classification
A hybrid ConvNeXt and Random Walk Grey Wolf Optimizer framework classified six categories of cotton leaf conditions with 98.39% accuracy ...
A hybrid ConvNeXt and Random Walk Grey Wolf Optimizer framework classified six categories of cotton leaf conditions with 98.39% accuracy ...
A new deep learning framework that pairs super-resolution image enhancement with swarm-optimized transfer learning classified Alzheimer's disease stages from MRI ...
A transfer learning method tested in Iran's Kerman magmatic arc identified all three known porphyry copper deposits in an evaluation ...
A response-surface-guided neural network predicted a balanced silica-fume and nano-silica formulation for coral aggregate concrete, reducing prediction error without additional ...
EfficientNet-B0 classified six pavement-image categories with 89.8% accuracy on the study dataset, outperforming ResNet50 and supporting a derived road-condition index.
Researchers in China converted well logs into images and used an attention-enhanced convolutional neural network to identify volcanic lithologies in ...
A new transfer learning framework measures cultural distance between audiences and uses it to rewrite film, tourism, and heritage content ...
A controlled study of four frontier chatbots building helmet-detection models finds that the most detailed prompts can trigger catastrophic training ...
Alzheimer's disease affects tens of millions of people worldwide, and the World Health Organization estimates that over 55 million people ...
A hybrid model pairing a ConvNeXt backbone with a Kolmogorov–Arnold Network head reaches 94.5 percent accuracy in four-way Alzheimer's staging ...
© 2026 Scienmag - Science Magazine
© 2026 Scienmag - Science Magazine