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1
Thermodynamics-based Artificial Neural Networks for constitutive modeling
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Thermodynamics-based Artificial Neural Networks for constitutive modeling

Journal of the mechanics and physics of solids, 2021-02, Vol.147 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0022-5096 ;DOI: 10.1016/j.jmps.2020.104277

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2
Solving multiple linear regression problem using artificial neural network
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Solving multiple linear regression problem using artificial neural network

International journal of electrical and computer engineering (Malacca, Malacca), 2022-02, Vol.12 (1), p.770

Copyright IAES Institute of Advanced Engineering and Science Feb 2022 ;ISSN: 2088-8708 ;EISSN: 2722-2578 ;EISSN: 2088-8708 ;DOI: 10.11591/ijece.v12i1.pp770-775

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3
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
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DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Genome Biology, 2017-04, Vol.18 (1), p.67-67, Article 67 [Peer Reviewed Journal]

2017. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2017 ;ISSN: 1474-760X ;ISSN: 1474-7596 ;EISSN: 1474-760X ;DOI: 10.1186/s13059-017-1189-z ;PMID: 28395661

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4
The structure of reconstructed flows in latent spaces
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The structure of reconstructed flows in latent spaces

Chaos (Woodbury, N.Y.), 2020-09, Vol.30 (9), p.093109-093109 [Peer Reviewed Journal]

Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 1054-1500 ;EISSN: 1089-7682 ;DOI: 10.1063/5.0013714 ;CODEN: CHAOEH

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5
Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D
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Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Proceedings of the National Academy of Sciences - PNAS, 2018-09, Vol.115 (36), p.9026-9031 [Peer Reviewed Journal]

Volumes 1–89 and 106–115, copyright as a collective work only; author(s) retains copyright to individual articles ;Copyright National Academy of Sciences Sep 4, 2018 ;2018 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1804420115 ;PMID: 30135100

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6
Editorial: From Pioneering Artificial Neural Networks to Deep Learning and Beyond
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Editorial: From Pioneering Artificial Neural Networks to Deep Learning and Beyond

International journal of neural systems, 2021-05, Vol.31 (5) [Peer Reviewed Journal]

2021, The Author(s) ;ISSN: 0129-0657 ;EISSN: 1793-6462 ;DOI: 10.1142/S0129065721030040

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7
Performance Analysis of Various Activation Functions in Artificial Neural Networks
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Performance Analysis of Various Activation Functions in Artificial Neural Networks

Journal of physics. Conference series, 2019-06, Vol.1237 (2), p.22030 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1237/2/022030

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8
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical problems in engineering, 2021, Vol.2021, p.1-15 [Peer Reviewed Journal]

Copyright © 2021 Quang Hung Nguyen et al. ;COPYRIGHT 2021 Hindawi Limited ;Copyright © 2021 Quang Hung Nguyen et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;ISSN: 1024-123X ;ISSN: 1563-5147 ;EISSN: 1563-5147 ;DOI: 10.1155/2021/4832864

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9
A Survey on the Explainability of Supervised Machine Learning
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A Survey on the Explainability of Supervised Machine Learning

The Journal of artificial intelligence research, 2021-01, Vol.70, p.245-317 [Peer Reviewed Journal]

2021. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at https://www.jair.org/index.php/jair/about ;ISSN: 1076-9757 ;EISSN: 1943-5037 ;DOI: 10.1613/jair.1.12228

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10
Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects
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Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects

Combustion and flame, 2020-10, Vol.220, p.119-129 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0010-2180 ;EISSN: 1556-2921 ;DOI: 10.1016/j.combustflame.2020.06.008

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11
A review on computational intelligence for identification of nonlinear dynamical systems
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A review on computational intelligence for identification of nonlinear dynamical systems

Nonlinear dynamics, 2020, Vol.99 (2), p.1709-1761 [Peer Reviewed Journal]

Springer Nature B.V. 2020 ;Nonlinear Dynamics is a copyright of Springer, (2020). All Rights Reserved. ;ISSN: 0924-090X ;EISSN: 1573-269X ;DOI: 10.1007/s11071-019-05430-7

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12
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
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Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.196 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11020196

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13
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Nature communications, 2018-06, Vol.9 (1), p.2383-12, Article 2383 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-018-04316-3 ;PMID: 29921910

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14
A graph-convolutional neural network model for the prediction of chemical reactivity
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A graph-convolutional neural network model for the prediction of chemical reactivity

Chemical science (Cambridge), 2019-01, Vol.10 (2), p.370-377 [Peer Reviewed Journal]

Copyright Royal Society of Chemistry 2019 ;ISSN: 2041-6520 ;EISSN: 2041-6539 ;DOI: 10.1039/c8sc04228d ;PMID: 30746086

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15
A CNN-BiLSTM-AM method for stock price prediction
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A CNN-BiLSTM-AM method for stock price prediction

Neural computing & applications, 2021-05, Vol.33 (10), p.4741-4753 [Peer Reviewed Journal]

Springer-Verlag London Ltd., part of Springer Nature 2020 ;Springer-Verlag London Ltd., part of Springer Nature 2020. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-020-05532-z

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16
A guide to machine learning for biologists
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Article
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A guide to machine learning for biologists

Nature reviews. Molecular cell biology, 2022-01, Vol.23 (1), p.40-55 [Peer Reviewed Journal]

2021. Springer Nature Limited. ;Springer Nature Limited 2021. ;ISSN: 1471-0072 ;EISSN: 1471-0080 ;DOI: 10.1038/s41580-021-00407-0 ;PMID: 34518686

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17
A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques
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A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Engineering with computers, 2021-07, Vol.37 (3), p.2089-2109 [Peer Reviewed Journal]

Springer-Verlag London Ltd., part of Springer Nature 2020 ;Springer-Verlag London Ltd., part of Springer Nature 2020. ;ISSN: 0177-0667 ;EISSN: 1435-5663 ;DOI: 10.1007/s00366-019-00930-x

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18
Quantum convolutional neural networks
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Quantum convolutional neural networks

Nature physics, 2019-12, Vol.15 (12), p.1273-1278 [Peer Reviewed Journal]

Copyright Nature Publishing Group Dec 2019 ;ISSN: 1745-2473 ;EISSN: 1745-2481 ;DOI: 10.1038/s41567-019-0648-8

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19
Efficient representation of quantum many-body states with deep neural networks
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Efficient representation of quantum many-body states with deep neural networks

Nature communications, 2017-09, Vol.8 (1), p.662-6, Article 662 [Peer Reviewed Journal]

2017. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2017 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-017-00705-2 ;PMID: 28939812

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20
Application of Artificial Neural Networks in Geoinformatics
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Application of Artificial Neural Networks in Geoinformatics

https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode ;ISBN: 3038427411 ;ISBN: 9783038427414 ;ISBN: 9783038427421 ;ISBN: 303842742X ;DOI: 10.3390/books978-3-03842-741-4

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