Results 161 to 170 of about 12,663 (268)
Cross-Architecture Knowledge Distillation for Histopathological Image Analysis. [PDF]
Boudissa S +3 more
europepmc +1 more source
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang +9 more
wiley +1 more source
Distilling Knowledge in Gastroenterology: An Artificial Intelligence System for Assisting Colonoscopy and Pathology Report Review. [PDF]
Mau B +5 more
europepmc +1 more source
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning. [PDF]
Alecu AA.
europepmc +1 more source
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti +4 more
wiley +1 more source
Parental psychological control and problematic smartphone use in Chinese early adolescents: shyness as a mediator and teacher-student relationship as a moderator. [PDF]
Sun H, Yu Y, Zhu M, Chen X, Si Y.
europepmc +1 more source
Overview of the proposed Gate‐Align‐SED, including two stages of training: (1) Mean‐Teacher SSL Training; and (2) Enhancer Model Training. In complex real‐world environments such as disaster monitoring, effective sound event detection (SED) is often hindered by the presence of noise and limited labeled data.
Jieli Chen +4 more
wiley +1 more source
Stand Up and Educate Ourselves on Academic Freedom. [PDF]
van der Leeuw RM +7 more
europepmc +1 more source
Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang +6 more
wiley +1 more source

