Results 81 to 90 of about 1,371,617 (304)

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

IEEE transactions on fuzzy systems
Machine learning draws its power from various disciplines, including computer science, cognitive science, and statistics. Although machine learning has achieved great advancements in both theory and practice, its methods have some limitations when ...
Jie Lu, Guangzhi Ma, Guangquan Zhang
semanticscholar   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Conditional density estimation with class probability estimators

, 2009
Many regression schemes deliver a point estimate only, but often it is useful or even essential to quantify the uncertainty inherent in a prediction. If a conditional density estimate is available, then prediction intervals can be derived from it.
Remco R. Bouckaert, Eibe Frank, Frank, Eibe, Bouckaert, Remco R.   +3 more
core   +1 more source

Leveraging Machine Learning for Official Statistics: A Statistical Manifesto

CoRR
It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the methodological robustness necessary to produce high quality statistical results.
Marco J. H. Puts, David Salgado, Piet J. H. Daas   +2 more
openaire   +2 more sources

Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing

Advanced Engineering Materials, EarlyView.
The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai, Zeki Ayrildi, Buse Şen, Maik Rajkowski, Mathias Redondo Garcia, Markus Stricker, Guillaume Laplanche   +6 more
wiley   +1 more source

Statistics and Machine Learning: Regression and Forecasting

, 2023
Under the direction of Dr. Julie Clark Statistics and machine learning are two methods for analyzing and modeling data. A dependent variable\u27s relationship to one or more independent variables is described through regression. The forecasting technique
Doan, Anna
core  

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Exploiting Machine Learning for Predicting Nodal Status in Prostate Cancer Patients [PDF]

, 2013
Prostate cancer is the second cause of cancer in males. The prophylactic pelvic irradiation is usually needed for treating prostate cancer patients with Subclinical Nodal Metestases.
B. De Bari, Stefano M. Magrini, M. Vallati, Jereczek-Fossa, Barbara,, Jereczek-Fossa, Barbara, Mauro Vallati, Gatta, Roberto, R. Gatta, F. Bertoni, MAGRINI, Stefano Maria, De Bari, Berardino, Filippo Bertoni, Bari, Berardino, De, Buglione, Michela, Bari, Berardino,, Magrini, Stefano, M., Magrini, Stefano M., Berardino De Bari, Magrini, Stefano, Roberto Gatta, Vallati, Mauro, Michela Buglione, BUGLIONE DI MONALE E BASTIA, Michela, Jereczek-Fossa, Barbara A., Jereczek-Fossa, Barbara, A., Magrini, Stefano,, Bertoni, Filippo, Barbara A. Jereczek-Fossa, B. A. Jereczek Fossa   +28 more
core   +1 more source