Results 271 to 280 of about 142,362 (315)

Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach. [PDF]

J Neuroeng Rehabil
Lee JY, Shim M, Chang WK, Cho HM, Choi JS, Kim H, Suh B, Paik NJ, Hwang HJ, Kim WS.   +9 more
europepmc   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy. [PDF]

BMC Med Inform Decis Mak
Gutmann AS, Mandl MM, Rieder C, Hoechter DJ, Dietz K, Geisler BP, Boulesteix AL, Tomasi R, Hinske LC.   +8 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Tabular prior-data fitted network in real-world CT radiomics: benign <i>vs.</i> malignant renal tumor classification. [PDF]

Quant Imaging Med Surg
Liu T, Wang H, Guo Y, Ye Y, Weng B, Li X, Chen J, Xie S, Zhong G, Song Z, Huang L.   +10 more
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning. [PDF]

Commun Eng
Wang H, Zhang X, Ren X, Zhang Z, Yang S, Lian C, Ma J, Zeng D.   +7 more
europepmc   +1 more source

Contextual quantum neural networks for stock price prediction. [PDF]

Sci Rep
Mourya S, Leipold H, Adhikari B.
europepmc   +1 more source

Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study.

JMIR Med Inform
Huang Y, Chen X, Bai G, Zhao Y, Kuang D, Zhang L, Lu W.   +6 more
europepmc   +1 more source

Portfolio Optimization: A Neurodynamic Approach Based on Spiking Neural Networks. [PDF]

Biomimetics (Basel)
Khan AH, Mohammed AM, Li S.
europepmc   +1 more source