Results 81 to 90 of about 28,526 (253)

Feature Selection in High Dimension Datasets using Incremental Feature Clustering

Indian Journal Of Science And Technology
Objectives: To develop a machine learning-based model to select the most important features from a high-dimensional dataset to classify the patterns at high accuracy and reduce their dimensionality. Methods: The proposed feature selection method (FSIFC) forms and combines feature clusters incrementally and produces feature subsets each time. The method
Damodar Patel, Amit Kumar Saxena
openaire   +1 more source

Creating Ti–Fe α/β Alloys by Diffusion‐Driven Solid‐State Processing

Advanced Engineering Materials, EarlyView.
This study proposes making alloys containing fast diffusing elements that are difficult to produce by ingot metallurgy, by diffusion‐driven solid‐state HIP processing of elemental powders and low‐temperature homogenisation. Here, novel Fe‐Ti α–β alloys are formed having fine α–β lamellae, a small β prior grain size without significant intermetallics ...
Jiaqi Xu, Xun Zhang, Jack M. Donoghue, Albert D. Smith, Rahul Unnikrishnan, Rhys Thomas, Vahid Nekouie, Jie Luo, Dongchen Hu, Michael Preuss, Philip J. Withers   +10 more
wiley   +1 more source

IFSFSPIS: Incremental Feature Selection and Feature Sensitive Progressive Instance Selection Models for BigData Analytics

International Journal of Intelligent Systems and Applications in Engineering, 2023
In this paper, a highly robust and first-of-its-kind Incremental Feature Selection (IFS) framework is designed for BigData Analytics, which considers both feature reduction as well as feature sensitive instance selection as a viable solution towards BigData Analytics. Unlike classical threshold based feature selection methods, this work is designed for
Kamble , Subhash, J. S., Arunalatha, K. R., Venugopal   +2 more
openaire   +1 more source

Rafting of Ni‐Based Superalloys Under Multiaxial Load as Understood by Phase‐Field Simulations and Critical Experiments

Advanced Engineering Materials, EarlyView.
Phase‐field simulations coupled with dislocation‐density‐based crystal plasticity modeling reproduce γ′ rafting behavior in single‐crystal Ni‐based superalloys under varied loading conditions. The model captures both macroscopic creep and microscopic morphology evolution, with results matching high‐temperature creep experiments.
Micheal Younan, David Bürger, Gunther Eggeler, Oleg Shchyglo, Ingo Steinbach, Muhammad Adil Ali   +5 more
wiley   +1 more source

Efficient Incremental-Ranked Feature Selection in Massive Data

, 2023
Comisión Interministerial de Ciencia y Tecnología TIN2004 ...
Ruiz Sánchez, Roberto, Aguilar Ruiz, Jesús Salvador, Riquelme Santos, José Cristóbal   +2 more
openaire   +2 more sources

Affecting the Properties of Copper–Graphene Electroconductive Composite by Severe Plastic Deformation

Advanced Engineering Materials, EarlyView.
Copper‐based composites enhanced with carbon feature convenient mechanical properties and favorable electric conductivity. Processing via deformation and thermomechanical treatments can introduce advantageous microstructures further enhancing their performance. Herein, copper–graphene powder‐based composites are directly consolidated via rotary swaging
Radim Kocich, Lenka Kunčická, Pavel Czernek, Zifan Wang   +3 more
wiley   +1 more source

Predicting A-to-I RNA editing by feature selection and random forest.

PLoS ONE, 2014
RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods.
Yang Shu, Ning Zhang, Xiangyin Kong, Tao Huang, Yu-Dong Cai   +4 more
doaj   +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

GBDT-IL: Incremental Learning of Gradient Boosting Decision Trees to Detect Botnets in Internet of Things

Sensors
The rapid development of the Internet of Things (IoT) has brought many conveniences to our daily life. However, it has also introduced various security risks that need to be addressed. The proliferation of IoT botnets is one of these risks.
Ruidong Chen, Tianci Dai, Yanfeng Zhang, Yukun Zhu, Xin Liu, Erfan Zhao   +5 more
doaj   +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