Wolf-Inspired AI Spots Finger Millet Diseases With 98% Accuracy
Researchers combined deep learning, grey wolf optimization, explainable heatmaps and a Gemini-powered chatbot to detect finger millet diseases with 98.36 ...
Researchers combined deep learning, grey wolf optimization, explainable heatmaps and a Gemini-powered chatbot to detect finger millet diseases with 98.36 ...
Every year, acute lymphoblastic leukemia claims thousands of lives, many of them children, and the first clue to its presence ...
Deep learning models that promise to weigh pastures from photographs may be far less trustworthy than their published accuracy figures ...
A 0.15-million-parameter attention-equipped neural network classifies nine wafer-map defect types with 99.88 percent accuracy on a CPU, combining latent-space augmentation ...
A new explainable deep learning architecture combines pyramid spatial atrous channel attention with ResNet50V2 to detect pulmonary fibrosis on chest ...
A sweeping systematic review maps how explainable artificial intelligence is being applied to schizophrenia diagnosis, prognosis, and monitoring, and outlines ...
A hybrid CNN–BiLSTM network with additive attention classifies brain MRI into four Alzheimer's disease stages with 95.94% slice-level accuracy, while ...
Researchers in India have built a unified deep learning framework that classifies brain tumors in MRI scans with 98.52 percent ...
Researchers at VIT-AP University have developed an explainable hybrid deep learning model that classifies knee osteoporosis from X-rays with 91 ...
Researchers have built a framework that turns AI explanation maps into a single score capable of predicting when a breast ...
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine