Results 231 to 240 of about 1,171,833 (351)

Potential for use of Al/machine learning for pharmacovigilance: Is there a role for regulators?

British Journal of Clinical Pharmacology, EarlyView.
Christina Gao, Ashley M. Hopkins, Andrew Rowland, Stephen Bacchi   +3 more
wiley   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source

Identifying congestion phenotypes using unsupervised machine learning in acute heart failure. [PDF]

Eur Heart J Digit Health
Rastogi T, Hutin O, Ter Maaten JM, Baudry G, Monzo L, Bresso E, Duarte K, Tromp J, Voors AA, Girerd N.   +9 more
europepmc   +1 more source

Efficient feature extraction for morphologically complex self-assembled porous microstructures using computational homology and unsupervised machine learning

Farshid Golnary, Mohsen Asghari
openalex   +1 more source

A Classical-Quantum Hybrid Approach for Unsupervised Probabilistic Machine Learning

, 2019
Prasanna Date, Catherine D. Schuman, Robert M. Patton, Thomas E. Potok   +3 more
openalex   +2 more sources

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Unveiling Twist Domains in Monolayer MoS₂ through 4D-STEM and Unsupervised Machine Learning. [PDF]

Small Methods
Kimoto K, Cretu O, Harano K, Uesugi F, Kikkawa J, Aso K, Oshima Y, Matsumoto T, Sakuma Y.   +8 more
europepmc   +1 more source

Identifying mutational signatures in individual cancer tumors using unsupervised machine learning

Bahar Mahdavi
openalex   +1 more source

Where Tech Meets the SDGs: A Supply‐Chain Process Map for Sustainability Management

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT This study investigates how advanced technologies support Sustainable Development Goals (SDGs) within supply chain management (SCM) through a structured analysis of 4448 sustainable practices. By integrating perspectives from sustainability‐oriented innovation (SOI) and contingent dynamic capabilities, the research conceptualizes technology ...
Vincenzo Varriale, Antonello Cammarano, Moacir Godinho‐Filho   +2 more
wiley   +1 more source

Correction: Sustainable visions: Unsupervised machine learning insights on global development goals. [PDF]

PLoS One
García-Rodríguez A, Núñez M, Pérez MR, Govezensky T, Barrio RA, Gershenson C, Kaski KK, Tagüeña J.   +7 more
europepmc   +1 more source