Transformer Meets BiLSTM as Nature-Inspired Feature Selection Boosts Software Fault Prediction
A hybrid Transformer-BiLSTM deep learning framework with a dual nature-inspired feature selection method reports up to 99 percent accuracy in ...
A hybrid Transformer-BiLSTM deep learning framework with a dual nature-inspired feature selection method reports up to 99 percent accuracy in ...
Researchers have developed X-URL, a character-level deep learning framework that classifies malicious URLs into four threat categories with over 98 ...
A two-stage Vision Transformer–U-Net pipeline restores reliable detection of rare voids in concrete X-ray CT images where single-stage deep learning ...
A new safety audit shows that machine unlearning in medical AI can silently degrade performance on rare diagnoses, and proposes ...
A new Cluster Computing study using stroke prediction shows that federated learning in healthcare will fail unless it confronts severe ...
A new study of three undergraduate courses shows that Moodle engagement predicts grades with up to 97 percent accuracy in ...
A new graph-attention-based generative adversarial network called GARAGE produces realistic synthetic single-cell RNA-seq data that preserves rare cell types and ...
A new classifier called CABLE-kNN caches local neighborhood evidence during training and neutralizes class priors, beating ordinary kNN on imbalanced ...
Researchers have introduced DACD, a dynamic adaptive learning method that detects concept drift in streaming data with a window-based CUSUM ...
A new study finds that the YOLO12s deep learning model detects mitotic figures in whole-slide breast cancer images with F1-scores ...
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© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine