Results 71 to 80 of about 3,311,851 (284)

Applying the relational modelling and knowledge-based techniques to the emitter database design

Journal of Telecommunications and Information Technology, 2003
The appropriate emitter database is one of the most important elements in the present electronic intelligence (ELINT) system. This paper provides an overview of the relational modelling, which is used to construct the emitter database for current ELINT ...
Janusz Dudczyk, Jan Matuszewski, Marian Wnuk   +2 more
doaj   +1 more source

Semantic networks [PDF]

COLING-02 on SEMANET building and using semantic networks -, 2002
In recent years, we have seen a surge of "Question and Answer" Systems (Answer Logic, iPhrase, Lingway, Albert, Baobab, Weniwen), triggered by the phenomenal IPO success of Ask Jeeves (www.ask.com). For the first time in a long time, linguists were being chased by professional headhunters and were commanding sky-high salaries. Then it all came down. Or
openaire   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Preliminary Data from the Small World of Singlish Words Project: Examining Responses to Common Singlish Words

Journal of Open Psychology Data
We report data from the Small World of Singlish Words (SWOSW) Project, where word associations are obtained for a list of common Singlish words. Singapore English, colloquially known as Singlish, is a dialect of English spoken in Singapore.
Jin Jye Wong, Cynthia S. Q. Siew
doaj   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source

HyperJournal software, PHP scripting and Semantic Web technologies for the Open Access [PDF]

, 2005
In this article we present an high level overview of the HyperJournal project, an effort to provide novel possibilities both in Scientific Publishing and in access to Scientific Contributions, according to the Open Access movement guidelines.
Barbera, Michele, Di Donato, Francesca, Tummarello, Giovanni, Morbidoni, Christian   +3 more
core  

Cognitive responsive e-assessment of constructive e-learning

Je-LKS: Journal of E-Learning and Knowledge Society, 2016
Semantic networks from 900 high education students from different knowledge domains were obtained by using a computer system. Then computer simulated schemata behavior and/or analysis of semantic networks allowed to select relevant course schemata ...
Guadalupe Elizabeth Morales-Martinez, Ernesto Octavio Lopez-Ramirez   +1 more
doaj   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning

Advanced Engineering Materials, EarlyView.
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose, Sanxchep Sharma, Veit Königer, Vincent Nebel, Jonas Oehm, Christian Weisenberger, Laura Bies, Volker Knoblauch   +7 more
wiley   +1 more source