Results 251 to 260 of about 60,311 (308)
Organizing across cognitive asymmetry in human–AI collaboration: A study of perfume creation
Abstract Research Summary As organizations increasingly adopt generative AI (GenAI), they face a strategic challenge: not only deciding which tasks AI should perform, but also how to organize the integration of human and AI efforts to produce viable solutions.
Tomoko Yokoi +3 more
wiley +1 more source
Relating transcriptomics to protein abundance reveals self-driven versus interactor-driven proteins. [PDF]
Arnaout L, Helms V, Papazian A.
europepmc +1 more source
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin +10 more
wiley +1 more source
This paper introduces a conceptual framework for an AI‐enhanced smart boiler suit, shifting maritime surveyor safety from reactive to proactive monitoring. Through integrated sensor networks and edge AI analytics, the proposed architecture offers a robust modular roadmap for advancing intelligent personal protective equipment in hazardous offshore and ...
P. Katsinellos +4 more
wiley +1 more source
DSDAN: A Dual-Stream Multimodal Log-Flow Fusion Framework for Network Configuration Auditing and Compliance-Risk Detection. [PDF]
Javeed MS +4 more
europepmc +1 more source
DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Extraction of brainprint by means of autoencoder with attention mechanism. [PDF]
Oztemel ME, Soysal ÖM.
europepmc +1 more source
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
wiley +1 more source
Projection-Free CLIP-Scale EEG Latents via a U-Net-Style Autoencoder. [PDF]
Lee J, Ahn J, Sim J, Kang H.
europepmc +1 more source

