Results 91 to 100 of about 8,368,743 (296)

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

CoRR, 2018
Poster, selected for the 2nd Reproducibility in Machine Learning Workshop at ICML 2018, Stockholm ...
Correa Publio, Gustavo, Esteves, Diego, Ławrynowicz, Agnieszka, Panov, Panče, Soldatova, Larisa, Soru, Tommaso, Vanschoren, J., Zafar, Hamid   +7 more
openaire   +3 more sources

The PRIMA Thesaurus for Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa, Alexey Boubnov, Dario De Angelis, Catriona Eschke, Maximilian Kabbe, Felix Kraus, Mirco Panighel, Richard Thelen, Davide Valentinis   +8 more
wiley   +1 more source

Learning Augmented Optimization: Faster, Better and Guaranteed

, 2019
Presented on November 20, 2019 at 12:15 p.m. in the Marcus Nanotechnology Building, Room 1116.Zhangyang (Atlas) Wang is an Assistant Professor of Computer Science and Engineering at Texas A&M University. Dr.
Wang, Zhangyang
core  

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Journal of Cardiovascular Magnetic Resonance, 2019
Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction.
Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft   +14 more
doaj   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Targeted Active Learning for Bayesian Decision-Making

Active learning is usually applied to acquire labels of informative data points in supervised learning, to maximize accuracy in a sample-efficient way.
Kaski, Samuel, Tiulpin, Aleksei, Filstroff, Louis, Sundin, Iiris, Kylmäoja, Juuso, Mikkola, Petrus   +5 more
core   +1 more source

On Leveraging Machine Learning in Sport Science in the Hypothetico-deductive Framework

Sports Medicine - Open
Supervised machine learning (ML) offers an exciting suite of algorithms that could benefit research in sport science. In principle, supervised ML approaches were designed for pure prediction, as opposed to explanation, leading to a rise in powerful, but ...
Jordan Rodu, Alexandra F. DeJong Lempke, Natalie Kupperman, Jay Hertel   +3 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach

IEEE Access
With the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible ...
Mohammed B. Alshawki, Ihab Ahmed Najm, Alaa Khalaf Hamoud   +2 more
doaj   +1 more source