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Search for "artificial neural network (ANN)" in Full Text gives 5 result(s) in Beilstein Journal of Nanotechnology.

A review of metal-organic frameworks and polymers in mixed matrix membranes for CO2 capture

  • Charlotte Skjold Qvist Christensen,
  • Nicholas Hansen,
  • Mahboubeh Motadayen,
  • Nina Lock,
  • Martin Lahn Henriksen and
  • Jonathan Quinson

Beilstein J. Nanotechnol. 2025, 16, 155–186, doi:10.3762/bjnano.16.14

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Published 12 Feb 2025

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

  • Shan He,
  • Julen Segura Abarrategi,
  • Harbil Bediaga,
  • Sonia Arrasate and
  • Humberto González-Díaz

Beilstein J. Nanotechnol. 2024, 15, 535–555, doi:10.3762/bjnano.15.47

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  • comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as
  • serve as valuable tools in the design of drug delivery systems for neurosciences. Keywords: artificial neural network (ANN); linear discriminant analysis (LDA); machine learning; nanoparticle; neurodegenerative diseases; Introduction Over time, there has been a significant shift in global dietary
  • to conduct experimental/theoretical studies on new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases [50]. Additionally, Romero Durán et al. expanded the AD database to develop artificial neural network (ANN) algorithms. These models were designed to forecast how ADs
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Published 15 May 2024

A superconducting adiabatic neuron in a quantum regime

  • Marina V. Bastrakova,
  • Dmitrii S. Pashin,
  • Dmitriy A. Rybin,
  • Andrey E. Schegolev,
  • Nikolay V. Klenov,
  • Igor I. Soloviev,
  • Anastasiya A. Gorchavkina and
  • Arkady M. Satanin

Beilstein J. Nanotechnol. 2022, 13, 653–665, doi:10.3762/bjnano.13.57

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  • one of the main applications of modern quantum processors [1][2][3][4][5][6][7][8][9]. It has been shown that a relatively small quantum circuit may be capable of searching for a large number of synaptic weights of an artificial neural network (ANN) [10][11][12][13]. The rate of the weight adjustment
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Published 14 Jul 2022

A review on the green and sustainable synthesis of silver nanoparticles and one-dimensional silver nanostructures

  • Sina Kaabipour and
  • Shohreh Hemmati

Beilstein J. Nanotechnol. 2021, 12, 102–136, doi:10.3762/bjnano.12.9

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Published 25 Jan 2021

Controlling the proximity effect in a Co/Nb multilayer: the properties of electronic transport

  • Sergey Bakurskiy,
  • Mikhail Kupriyanov,
  • Nikolay V. Klenov,
  • Igor Soloviev,
  • Andrey Schegolev,
  • Roman Morari,
  • Yury Khaydukov and
  • Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2020, 11, 1336–1345, doi:10.3762/bjnano.11.118

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  • facilitates new possibilities for the development of tunable superconducting electronic components. For example, the considered “stranded wire” can be readily applied in a synaptic connection for a superconducting artificial neural network (ANN), where the information is represented in a “current domain” [10
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Published 07 Sep 2020
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