Results 131 to 140 of about 3,700 (260)

Pattern recognition in living cells through the lens of machine learning. [PDF]

Open Biol
Britto Bisso F, Aguilar R, Shree D, Zhu Y, Espinoza M, Diaz B, Cuba Samaniego C.   +6 more
europepmc   +1 more source

A Graph‐Based Generative Artificial Intelligence Methodology for Autocorrection of Utility‐System P&IDs

Chemie Ingenieur Technik, EarlyView.
This work explores generative AI for automated revision of Piping and Instrumentation Diagrams (P&IDs). We frame P&ID correction as a translation problem, converting attributed P&ID graphs into sequences and learning revisions with a transformer‐based model.
Lukas Schulze Balhorn, Dominik P. Goldstein, Niels Seijsener, Kevin Dao, Ge H. M. Driessen, Artur M. Schweidtmann   +5 more
wiley   +1 more source

Point cloud generation adversarial network based on self-attention and curvature. [PDF]

PLoS One
Sun F, Shen C, Kong Y, Zhang Z, Hu M, Xiong F.   +5 more
europepmc   +1 more source

Nonlinear permuted Granger causality

Canadian Journal of Statistics, EarlyView.
Abstract Granger causality is an established, contentious method that seeks causal temporal connections via association and precedence. While not true causal inference, it assists in mapping networks of information flow that may warrant further study.
Noah D. Gade, Jordan Rodu
wiley   +1 more source

Polynomial Perceptrons for Compact, Robust, and Interpretable Machine Learning Models. [PDF]

Entropy (Basel)
Aldana-Bobadilla E, Molina-Villegas A, Cesar-Hernandez J, Garza-Fabre M.   +3 more
europepmc   +1 more source

Bridging Theory and Prediction: A Hybrid SEM and Machine Learning Approach to Optimize Lean Construction for Megaproject Sustainability in China

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Construction megaprojects, large‐scale, complex, and capital‐intensive, are particularly prone to inefficiencies, cost overruns, delays, and environmental degradation due to fragmented workflows, stakeholder misalignment, and resource intensity.
Abdelazim Ibrahim, Tarek Zayed, Zoubeir Lafhaj, Ahmed Maged, Ahmed Farouk Kineber, Jingchao Yang   +5 more
wiley   +1 more source

Interpretable machine learning for thermoelectric materials design with Kolmogorov-Arnold networks. [PDF]

Sci Rep
Fronzi M, Ford MJ, Nayal KS, Isayev O, Stampfl C.   +4 more
europepmc   +1 more source

Combating ESG Greenwashing Through AI Models: Evidence From Disaggregated AI Technologies, Mechanisms, and Thresholds

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study examines how artificial intelligence language models influence corporate environmental, social, and governance greenwashing (GWESG$$ {\mathrm{GW}}_{\mathrm{ESG}} $$) behavior, utilizing panel data from Chinese listed firms spanning 2012–2022.
Brahim Bergougui, Hamid Ghazi H. Sulimany, Abdulrahman Atllah Alharbi   +2 more
wiley   +1 more source

Multilayer perceptron neural network-genetic algorithm for modeling Nicotiana tabacum leaf quality. [PDF]

PLoS One
Najafi MR, Aghajani MA, Safaie N, Rahmanpour S.   +3 more
europepmc   +1 more source

DinoFlow: Self‐supervised pretraining in flow cytometry enables accurate detection of common hematopathological disorders

Cytometry Part B: Clinical Cytometry, EarlyView.
Abstract Flow cytometry is an essential component of routine hematological lab testing. Many computational methods have been proposed for the analysis of flow cytometry data, but most have focused on supervised learning for just one or a few specific disorders.
Brendan O'Fallon, Muir Morrison, Mattia Medina Grespan, Nicholas C. Spies, David P. Ng   +4 more
wiley   +1 more source