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Results 1 - 20 of 40,803  for All Library Resources

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1
AI-based pathology predicts origins for cancers of unknown primary
AI-based pathology predicts origins for cancers of unknown primary
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AI-based pathology predicts origins for cancers of unknown primary

Nature (London), 2021-06, Vol.594 (7861), p.106-110 [Peer Reviewed Journal]

Copyright Nature Publishing Group Jun 3, 2021 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-021-03512-4 ;PMID: 33953404

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2
A comparative analysis of gradient boosting algorithms
A comparative analysis of gradient boosting algorithms
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A comparative analysis of gradient boosting algorithms

The Artificial intelligence review, 2021-03, Vol.54 (3), p.1937-1967 [Peer Reviewed Journal]

Springer Nature B.V. 2020 ;COPYRIGHT 2021 Springer ;Springer Nature B.V. 2020. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-020-09896-5

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3
Improving the accuracy of medical diagnosis with causal machine learning
Improving the accuracy of medical diagnosis with causal machine learning
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Improving the accuracy of medical diagnosis with causal machine learning

Nature communications, 2020-08, Vol.11 (1), p.3923-3923, Article 3923 [Peer Reviewed Journal]

The Author(s) 2020. 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) 2020. corrected publication 2021. 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) 2020, corrected publication 2021 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-17419-7 ;PMID: 32782264

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4
FCHL revisited: Faster and more accurate quantum machine learning
FCHL revisited: Faster and more accurate quantum machine learning
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FCHL revisited: Faster and more accurate quantum machine learning

The Journal of chemical physics, 2020-01, Vol.152 (4), p.044107-044107 [Peer Reviewed Journal]

Author(s) ;2020 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/1.5126701 ;PMID: 32007071 ;CODEN: JCPSA6

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5
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Statistical and Machine Learning forecasting methods: Concerns and ways forward
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Statistical and Machine Learning forecasting methods: Concerns and ways forward

PloS one, 2018-03, Vol.13 (3), p.e0194889-e0194889 [Peer Reviewed Journal]

COPYRIGHT 2018 Public Library of Science ;COPYRIGHT 2018 Public Library of Science ;2018 Makridakis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018 Makridakis et al 2018 Makridakis et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0194889 ;PMID: 29584784

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6
Deep learning based detection and analysis of COVID-19 on chest X-ray images
Deep learning based detection and analysis of COVID-19 on chest X-ray images
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Deep learning based detection and analysis of COVID-19 on chest X-ray images

Applied intelligence (Dordrecht, Netherlands), 2021-03, Vol.51 (3), p.1690-1700 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2020 ;Springer Science+Business Media, LLC, part of Springer Nature 2020. ;ISSN: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-020-01902-1 ;PMID: 34764553

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7
Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
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Uniformly accurate machine learning-based hydrodynamic models for kinetic equations

Proceedings of the National Academy of Sciences - PNAS, 2019-10, Vol.116 (44), p.21983-21991 [Peer Reviewed Journal]

Copyright National Academy of Sciences Oct 29, 2019 ;2019 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1909854116 ;PMID: 31619568

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8
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images
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Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computers in biology and medicine, 2021-05, Vol.132, p.104319-104319, Article 104319 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104319 ;PMID: 33799220

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9
Predicting and Evaluating the Online News Popularity based on Random Forest
Predicting and Evaluating the Online News Popularity based on Random Forest
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Predicting and Evaluating the Online News Popularity based on Random Forest

Journal of physics. Conference series, 2021-08, Vol.1994 (1), p.12040 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. 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: 10.1088/1742-6596/1994/1/012040

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10
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
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Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

Protein science, 2022-08, Vol.31 (8), p.e4379-n/a [Peer Reviewed Journal]

2022 The Authors. published by Wiley Periodicals LLC on behalf of The Protein Society. ;2022. This article 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. ;ISSN: 0961-8368 ;EISSN: 1469-896X ;DOI: 10.1002/pro.4379 ;PMID: 35900023

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11
Model Extraction Attack and Defense on Deep Generative Models
Model Extraction Attack and Defense on Deep Generative Models
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Model Extraction Attack and Defense on Deep Generative Models

Journal of physics. Conference series, 2022-02, Vol.2189 (1), p.12024 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. 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: 10.1088/1742-6596/2189/1/012024

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12
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy
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OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

The Journal of chemical physics, 2021-11, Vol.155 (20), p.204103-204103 [Peer Reviewed Journal]

Author(s) ;2021 Author(s). Published under an exclusive license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0061990 ;CODEN: JCPSA6

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13
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
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The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

BioData mining, 2021-02, Vol.14 (1), p.13-13, Article 13 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. 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) 2021 ;ISSN: 1756-0381 ;EISSN: 1756-0381 ;DOI: 10.1186/s13040-021-00244-z ;PMID: 33541410

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14
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
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Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

The Journal of chemical physics, 2016-10, Vol.145 (16), p.161102-161102 [Peer Reviewed Journal]

Author(s) ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/1.4964627 ;PMID: 27802646 ;CODEN: JCPSA6

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15
Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials
Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials
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Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials

The Journal of chemical physics, 2020-06, Vol.152 (23), p.234102-234102 [Peer Reviewed Journal]

Author(s) ;2020 Author(s). Published under license by AIP Publishing. ;ISSN: 0021-9606 ;EISSN: 1089-7690 ;DOI: 10.1063/5.0009491 ;CODEN: JCPSA6

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16
Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics
Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics
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Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics

Nature communications, 2020-08, Vol.11 (1), p.4334-4334, Article 4334 [Peer Reviewed Journal]

The Author(s) 2020. 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) 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-18171-8 ;PMID: 32859911

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17
Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning
Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning
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Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning

Geophysical research letters, 2020-07, Vol.47 (13), p.n/a [Peer Reviewed Journal]

2020. The Authors. ;2020. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0094-8276 ;EISSN: 1944-8007 ;DOI: 10.1029/2020GL088229

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18
A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score
A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score
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A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Entropy (Basel, Switzerland), 2020-12, Vol.22 (12), p.1412 [Peer Reviewed Journal]

2020. This work is licensed 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. ;2020 by the authors. 2020 ;ISSN: 1099-4300 ;EISSN: 1099-4300 ;DOI: 10.3390/e22121412 ;PMID: 33333829

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19
Prediction of cryptocurrency returns using machine learning
Prediction of cryptocurrency returns using machine learning
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Prediction of cryptocurrency returns using machine learning

Annals of operations research, 2021-02, Vol.297 (1-2), p.3-36 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2020 ;COPYRIGHT 2021 Springer ;Springer Science+Business Media, LLC, part of Springer Nature 2020. ;ISSN: 0254-5330 ;EISSN: 1572-9338 ;DOI: 10.1007/s10479-020-03575-y

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20
Selecting critical features for data classification based on machine learning methods
Selecting critical features for data classification based on machine learning methods
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Selecting critical features for data classification based on machine learning methods

Journal of big data, 2020-07, Vol.7 (1), p.1-26, Article 52 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. 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. ;ISSN: 2196-1115 ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-020-00327-4

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