Results 231 to 240 of about 13,735 (297)

AI‐Revealed Transition From Density‐ to Connectivity‐Controlled Mechanics in Hierarchical Porous Materials

Small, EarlyView.
Using synchrotron‐based multiscale imaging, generative modeling algorithms, and computational mechanics models, our study reveals a scale‐dependent transition in bone mechanics. The results of the power‐scaling law illustrate a transition from density‐controlled stiffness at the trabecular scale to connectivity‐controlled, porosity‐sensitive mechanics ...
Milad Masrouri, Federica Buccino, Laura Maria Vergani, Zhao Qin   +3 more
wiley   +1 more source

Uncovering hidden factors of cognitive resilience in Alzheimer's disease using a conditional-Gaussian mixture variational autoencoder. [PDF]

NPJ Dement
Cao Y, Chukkapalli Y, Canchi S, Umashankar V, Luquez T, Janve V, Hohman TJ, Menon V, Draelos A, Kaczorowski CC.   +9 more
europepmc   +1 more source

A Large Language Model‐Based Approach for Fault Detection and Its Application

Safety Science and Technology, EarlyView.
This work proposes an interpretable fault detection framework utilizing pre‐trained large language models to overcome small sample sizes and label scarcity in industrial datasets. A stepwise tuple‐based validation mitigates hallucinations, ensuring reliable detection.
Yihua Ye, Yin Zhu, Liming Che, Hua Zhou
wiley   +1 more source

Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers. [PDF]

Phys Eng Sci Med
Al Zegair F, Betz-Stablein B, Janda M, Soyer HP, Chandra SS.   +4 more
europepmc   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
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

Variational autoencoder enhanced analysis of energy metabolism and autophagy in exercising cardiomyocytes. [PDF]

Exp Biol Med (Maywood)
Chen L, Yang Y.
europepmc   +1 more source

From prediction to intervention: Paradigm shifts in causal AI for precision medicine and large‐scale cohorts

VIEW, EarlyView.
Large‐scale cohorts and multimodal biomedical data have enabled powerful predictive models for clinical risk stratification, but prediction alone cannot guide effective interventions. This review introduces causal artificial intelligence as a design‐first framework that integrates target trial emulation, causal discovery, and robust effect estimation ...
Linlin Cao, Yudian Xu, Yifan Chen, Honghao Huang, Wei Xu, Jun Pu   +5 more
wiley   +1 more source

Precision phenotyping of type 2 diabetes in chinese populations using a variational autoencoder-informed tree model. [PDF]

Nat Commun
Yue T, Zhang W, Ding Y, Zheng X, Ma Y, Chan JCN, Lau ESH, Lui JNM, Jin G, Xu W, Bi Y, Wang Z, Nie S, Gong M, Pearson ER, Luo S, Weng J.   +16 more
europepmc   +1 more source

Nanomedicine applications in lymphoma: Advancing precision diagnostics, targeted therapeutics, and prospective developments

VIEW, EarlyView.
Lymphoma is a group of blood cancers that can appear in lymph nodes, blood, bone marrow, spleen, liver, or the central nervous system, which makes drug delivery and disease monitoring difficult. This review summarizes how nanomedicine technologies may improve targeted treatment and imaging, while carefully separating approved or guideline‐supported ...
Mohd Ahmar Rauf, Yara Mashal, Afeefa Rao, Nasar Ata, Arun K. Iyer, Farhan Shah   +5 more
wiley   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source