The Canadian Journal of Chemical Engineering, EarlyView.This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
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Mapping Current Use of Artificial Intelligence in Pharmacology Education via a Scoping Review. [PDF]
Pharmacol Res PerspectCordier W, Ijeoma P.
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OEMA: ontology-enhanced multi-agent collaboration framework for zero-shot clinical named entity recognition. [PDF]
JAMIA OpenTao X, Dong X, Zhu Q, Zhou X.
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An integrated evolution-aware meta-learning framework with adversarial morphological augmentation for zero-day threat detections. [PDF]
Front Artif IntellLanka K, Shaik K.
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Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification. [PDF]
Diagnostics (Basel)Nouyed MI, Al-Mamun M, Adjeroh DA, Hu G.
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Cross-domain zero-shot semantic segmentation for unstructured environments via EVA-CLIP model, ensemble prompt engineering, and optimized text-image matching. [PDF]
PLoS OneZhou N, Zhao X, Zhou F, Li J, Yue X.
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Time-Conditioned Zero-Shot Self-Supervised Reconstruction for Accelerated 3D Ultra-Low-Field MRI. [PDF]
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Incremental Zero-Shot Learning
IEEE Transactions on Cybernetics, 2022The goal of zero-shot learning (ZSL) is to recognize objects from unseen classes correctly without corresponding training samples. The existing ZSL methods are trained on a set of predefined classes and do not have the ability to learn from a stream of training data.
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