Results 71 to 80 of about 273,096 (313)

Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks

AI, 2023
Perhaps one of the best-known machine learning models is the artificial neural network, where a number of parameters must be adjusted to learn a wide range of practical problems from areas such as physics, chemistry, medicine, etc.
Ioannis G. Tsoulos, Alexandros Tzallas
doaj   +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

Software Toolkit For Designing An Artificial Neural Network. [PDF]

, 2004
Basically, there are two kinds of artificial neural network (ANN), which can be classified into supervised and unsupervised. Commonly, supervised neural networks are trained or weights adjusted, so that a particular input leads to a specific target ...
Ahmad, M. A., Saleh, J Mohamad
core  

Bionic Artificial Neural Networks in Medical Image Analysis

Biomimetics, 2023
Bionic artificial neural networks (BANNs) are a type of artificial neural network (ANN) [...]
Shuihua Wang, Huiling Chen, Yudong Zhang
doaj   +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

Artificial Neural Networks Manipulation Server: Research on the Integration of Databases and Artificial Neural Networks [PDF]

Neural Computing & Applications, 2002
The final publication is available at Springer via http://dx.doi.org/10.1007 ...
Antonino Santos, Bernardino Arcay Varela, Julián Dorado, A. B. Rodríguez, Alejandro Pazos   +4 more
openaire   +3 more sources

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Role of Artificial Neural Networks in Dermatology [PDF]

Dermatology, 2009
based systems is the learning capacity of an ANN. At the very beginning of a training process, an ANN contains no explicit information. Then a large number of cases with a known outcome are presented to the system, and the weights of the interneuronal connections are changed by a training algorithm designed to minimize the total error of the system [1 ...
Renders, Jean-Michel, Simonart, Thierry
openaire   +3 more sources

Advancing Neural Networks: Innovations and Impacts on Energy Consumption

Advanced Electronic Materials
The energy efficiency of Artificial Intelligence (AI) systems is a crucial and actual issue that may have an important impact on an ecological, economic and technological level.
Alina Fedorova, Nikola Jovišić, Jordi Vallverdù, Silvia Battistoni, Miloš Jovičić, Milovan Medojević, Alexander Toschev, Evgeniia Alshanskaia, Max Talanov, Victor Erokhin   +9 more
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