Results 121 to 130 of about 212,260 (313)
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 +5 more
wiley +1 more source
SVM-based PQ disturbance recognition system
, 2017 The quality of power delivered by modern electricity grids is of interest as disturbances to power quality (PQ) have the potential to cause malfunction of control systems, interfere with communication networks, increase power losses and reduce the life ...
Michael Negnevitsky (14745250) +3 more
core
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 +3 more
wiley +1 more source
Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation
Advanced Intelligent Discovery, EarlyView.We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley +1 more source
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
Making Indefinite Kernel Learning Practical [PDF]
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems ...
Mierswa, Ingo
core
Advanced Intelligent Discovery, EarlyView.Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang +3 more
wiley +1 more source
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition-1
, 2011 Copyright information:Taken from "SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition"http://www.biomedcentral.com/1471-2105/8/S4/S2BMC Bioinformatics 2007;8(Suppl 4):S2-S2.Published online 22 May 2007PMCID:PMC1892081.
Rui Kuang (74290) +5 more
core +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition-8
, 2011 Copyright information:Taken from "SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition"http://www.biomedcentral.com/1471-2105/8/S4/S2BMC Bioinformatics 2007;8(Suppl 4):S2-S2.Published online 22 May 2007PMCID:PMC1892081.
Rui Kuang (74290) +5 more
core +1 more source