Results 131 to 140 of about 21,772 (300)

Enhancing classification performance over noise and imbalanced data problems

, 2012
This research presents the development of techniques to handle two issues in data classification: noise and imbalanced data problems. Noise is a significant problem that can degrade the quality of training data in any learning algorithm.
Jeatrakul, Piyasak
core  

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

Additive Manufacturing of Continuous Fibre Reinforced Composites: Process, Characterisation, Modelling, and Sustainability

Advanced Engineering Materials, EarlyView.
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley   +1 more source

Attribute Interactions in Medical Data Analysis

, 2003
There is much empirical evidence about the success of naive Bayesian classification (NBC) in medical applications of attribute-based machine learning. NBC assumes conditional independence between attributes. In classification, such classifiers sum up the
Ivan Bratko, Bratko, Ivan, Smrke, Dragica, Zupan, Blaz, Aleks Jakulin, Jakulin, Aleks, Demšar, Janez, Dragica Smrke, Blaž Zupan, Janez Demšar   +9 more
core   +1 more source

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

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

Exploration of Pseudobinary Alloys (Al–Mn)‐(Co–Fe–Ni) to Design High‐Entropy and Compositionally Complex Alloys to Stabilize the τ Phase in Al–Mn‐Based Ferromagnetic Alloys

Advanced Engineering Materials, EarlyView.
This experimental study investigates the thermodynamic limits of the Al–Mn τ ferromagnetic phase within complex‐composition alloys (CCAs). Using nine rare‐earth‐free compositions, it explores a broad region of the pseudobinary AlMnCoFeNi system. The results reveal intricate links between composition, phase stability, and magnetic behavior, highlighting
Sacha Plagnol‐Chauzu, Laurent Peltier, Fodil Meraghni, Stéphane Delalande, Frédéric Mazaleyrat   +4 more
wiley   +1 more source

Cascade framework for software fault prediction using ABC-based feature selection and SMOTE

Discover Artificial Intelligence
Software fault prediction (SFP) improves software reliability and reduces maintenance costs, but real-world datasets often suffer from class imbalance and redundant features, which limit model performance.
S. Karthik, S. Kaliraj, M. Dhasny Lydia
doaj   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Composites of Shellac and Silver Nanowires as Flexible, Biobased, and Corrosion‐Resistant Transparent Conductive Electrodes

Advanced Functional Materials, EarlyView.
Shellac, a centuries‐old natural resin, is reimagined as a green material for flexible electronics. When combined with silver nanowires, shellac films deliver transparency, conductivity, and stability against humidity. These results position shellac as a sustainable alternative to synthetic polymers for transparent conductors in next‐generation ...
Rahaf Nafez Hussein, Tiago Carneiro Gomes, Eliza Ng, Simon Rondeau‐Gagné, Tricia Breen Carmichael   +4 more
wiley   +1 more source