Results 71 to 80 of about 88,867 (255)

Flexural Behavior of 3D‐Printed Porous Bouligand‐Structured Polymers: Influence of Pitch Angle and Porosity

Advanced Engineering Materials, EarlyView.
This study examines how helicoidal architectures with different porosities respond to bending. Adjusting layer angle and spacing in 3D‐printed polymers reveals clear tradeoffs between stiffness, strength, and energy absorption. Experiments and simulations highlight designs that distribute stress effectively, offering pathways for optimizing lightweight
Praveenkumar Subhash Patil, Edward D. McCarthy, Parvez Alam   +2 more
wiley   +1 more source

Ensemble Approach Using k-Partitioned Isolation Forests for the Detection of Stock Market Manipulation

Mathematics
Stock market manipulation, defined as any attempt to artificially influence stock prices, poses significant challenges by causing financial losses and eroding investor trust.
Hugo Núñez Delafuente, César A. Astudillo, David Díaz   +2 more
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Isolation Forests for Symbolic Data as a Tool for Outlier Mining

Ekonometria
Aim: Outlier detection is a key part of every data analysis. Although many definitions of outliers can be found in the literature, all of them emphasize that outliers are objects that are in some way different from other objects in the dataset.
Marcin Pełka, Andrzej Dudek
doaj  

Isolation forests: looking beyond tree depth

CoRR, 2021
The isolation forest algorithm for outlier detection exploits a simple yet effective observation: if taking some multivariate data and making uniformly random cuts across the feature space recursively, it will take fewer such random cuts for an outlier to be left alone in a given subspace as compared to regular observations.
openaire   +2 more sources

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

Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques

IEEE Access
Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats.
Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat   +5 more
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

A Lightweight AI-Based Approach for Drone Jamming Detection

Future Internet
The future integration of drones in 6G networks will significantly enhance their capabilities, enabling a wide range of new applications based on autonomous operation.
Sergio Cibecchini, Francesco Chiti, Laura Pierucci   +2 more
doaj   +1 more source

Hybrid Isolation Forest - Application to Intrusion Detection

CoRR, 2017
24 pages, working ...
Marteau, Pierre-François, Soheily-Khah, Saeid, Béchet, Nicolas   +2 more
openaire   +4 more sources