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1
Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology
Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology
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Article 		Add to My Research

Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.), 2019-05, Vol.8 (3), p.264-272 [Peer Reviewed Journal]

Copyright 2019 Asia-Pacific Academy of Ophthalmology. ;ISSN: 2162-0989 ;EISSN: 2162-0989 ;DOI: 10.22608/APO.2018479 ;PMID: 31149787

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2
Learning Algorithms in a Decentralized General Equilibrium Model
Learning Algorithms in a Decentralized General Equilibrium Model
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Book 		Add to My Research

Learning Algorithms in a Decentralized General Equilibrium Model

Digital Resources/Online E-Resources

3
How the machine ‘thinks’: Understanding opacity in machine learning algorithms
How the machine ‘thinks’: Understanding opacity in machine learning algorithms
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Article 		Add to My Research

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

Big data & society, 2016-01, Vol.3 (1), p.205395171562251 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 2053-9517 ;EISSN: 2053-9517 ;DOI: 10.1177/2053951715622512

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4
Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms-are they on par with humans for diagnosing fractures?
Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms-are they on par with humans for diagnosing fractures?
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Artificial intelligence for analyzing orthopedic trauma radiographs: Deep learning algorithms-are they on par with humans for diagnosing fractures?

Acta orthopaedica, 2017-11, Vol.88 (6), p.581-586 [Peer Reviewed Journal]

2017 The Author(s). Published by Taylor & Francis on behalf of the Nordic Orthopedic Federation. 2017 ;ISSN: 1745-3674 ;ISSN: 1745-3682 ;EISSN: 1745-3682 ;DOI: 10.1080/17453674.2017.1344459 ;PMID: 28681679

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5
Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S
Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S
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Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S

Water resources research, 2017-05, Vol.53 (5), p.3878-3895 [Peer Reviewed Journal]

2017. The Authors. ;2017. American Geophysical Union. All Rights Reserved. ;ISSN: 0043-1397 ;EISSN: 1944-7973 ;DOI: 10.1002/2016WR019933

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6
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets
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Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Briefings in bioinformatics, 2021-07, Vol.22 (4) [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020 ;The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. ;The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com ;ISSN: 1467-5463 ;EISSN: 1477-4054 ;DOI: 10.1093/bib/bbaa321 ;PMID: 33313673

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7
Reply to: ‘Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms’ and ‘Confounding factors need to be accounted for in assessing bias by machine learning algorithms’
Reply to: ‘Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms’ and ‘Confounding factors need to be accounted for in assessing bias by machine learning algorithms’
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Reply to: ‘Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms’ and ‘Confounding factors need to be accounted for in assessing bias by machine learning algorithms’

Nature medicine, 2022-06, Vol.28 (6), p.1161-1162 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Nature America, Inc. 2022. ;ISSN: 1078-8956 ;EISSN: 1546-170X ;DOI: 10.1038/s41591-022-01854-8

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8
Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy
Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy
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Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy

Circulation. Cardiovascular imaging, 2016-06, Vol.9 (6) [Peer Reviewed Journal]

2016 American Heart Association, Inc. ;ISSN: 1941-9651 ;EISSN: 1942-0080 ;DOI: 10.1161/CIRCIMAGING.115.004330 ;PMID: 27266599

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9
A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China
A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China
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Article 		Add to My Research

A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China

Acta geotechnica, 2021-12, Vol.16 (12), p.4061-4080 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. ;ISSN: 1861-1125 ;EISSN: 1861-1133 ;DOI: 10.1007/s11440-021-01383-7

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10
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
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Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

Di xue qian yuan., 2020-11, Vol.11 (6), p.2207-2219 [Peer Reviewed Journal]

2020 China University of Geosciences (Beijing) and Peking University ;Copyright © Wanfang Data Co. Ltd. All Rights Reserved. ;ISSN: 1674-9871 ;DOI: 10.1016/j.gsf.2020.03.005

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11
Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox?
Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox?
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Article 		Add to My Research

Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox?

Neurology, 2023-03, Vol.100 (12), p.549-550 [Peer Reviewed Journal]

ISSN: 0028-3878 ;EISSN: 1526-632X ;DOI: 10.1212/WNL.0000000000206808 ;PMID: 36639238

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12
Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships
Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships
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Article 		Add to My Research

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

Journal of applied physics, 2020-10, Vol.128 (13) [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: 0021-8979 ;EISSN: 1089-7550 ;DOI: 10.1063/5.0013720 ;CODEN: JAPIAU

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13
Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach
Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach
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Using Machine Learning Algorithms to Predict People's Intention to Use Mobile Learning Platforms During the COVID-19 Pandemic: Machine Learning Approach

JMIR medical education, 2021-02, Vol.7 (1), p.e24032-e24032 [Peer Reviewed Journal]

Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. ;2021. 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. ;Iman Akour, Muhammad Alshurideh, Barween Al Kurdi, Amel Al Ali, Said Salloum. Originally published in JMIR Medical Education (http://mededu.jmir.org), 04.02.2021. 2021 ;ISSN: 2369-3762 ;EISSN: 2369-3762 ;DOI: 10.2196/24032 ;PMID: 33444154

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14
Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients
Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients
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Article 		Add to My Research

Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients

European heart journal, 2019-04, Vol.40 (13), p.1069-1077 [Peer Reviewed Journal]

Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com. 2019 ;Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com. ;ISSN: 0195-668X ;EISSN: 1522-9645 ;DOI: 10.1093/eurheartj/ehy915 ;PMID: 30689812

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15
Facial geometric feature extraction based emotional expression classification using machine learning algorithms
Facial geometric feature extraction based emotional expression classification using machine learning algorithms
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Article 		Add to My Research

Facial geometric feature extraction based emotional expression classification using machine learning algorithms

PloS one, 2021-02, Vol.16 (2), p.e0247131-e0247131 [Peer Reviewed Journal]

COPYRIGHT 2021 Public Library of Science ;COPYRIGHT 2021 Public Library of Science ;2021 M., A. 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. ;2021 M., A 2021 M., A ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0247131 ;PMID: 33600467

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16
GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms
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Article 		Add to My Research

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Di xue qian yuan., 2021-03, Vol.12 (2), p.857-876 [Peer Reviewed Journal]

2021 Elsevier B.V. ;Copyright Elsevier Science Ltd. Mar 2021 ;Copyright © Wanfang Data Co. Ltd. All Rights Reserved. ;ISSN: 1674-9871 ;EISSN: 2588-9192 ;DOI: 10.1016/j.gsf.2020.09.004

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17
Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose
Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose
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Article 		Add to My Research

Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose

PloS one, 2018-10, Vol.13 (10), p.e0205872 [Peer Reviewed Journal]

COPYRIGHT 2018 Public Library of Science ;COPYRIGHT 2018 Public Library of Science ;2018 Ma 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 Ma et al 2018 Ma et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0205872 ;PMID: 30339708

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18
Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells
Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells
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Article 		Add to My Research

Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

PloS one, 2016-09, Vol.11 (9), p.e0163045-e0163045 [Peer Reviewed Journal]

COPYRIGHT 2016 Public Library of Science ;COPYRIGHT 2016 Public Library of Science ;2016 Park 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. ;2016 Park et al 2016 Park et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0163045 ;PMID: 27636719

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19
Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications
Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications
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Article 		Add to My Research

Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications

Industrial management + data systems, 2020-01, Vol.120 (1), p.164-195 [Peer Reviewed Journal]

Emerald Publishing Limited 2019 ;ISSN: 0263-5577 ;EISSN: 1758-5783 ;DOI: 10.1108/IMDS-07-2019-0361

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20
Treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features and machine learning algorithms in colorectal cancer
Treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features and machine learning algorithms in colorectal cancer
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Article 		Add to My Research

Treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features and machine learning algorithms in colorectal cancer

Medical physics (Lancaster), 2021-07, Vol.48 (7), p.3691-3701 [Peer Reviewed Journal]

2021 American Association of Physicists in Medicine ;2021 American Association of Physicists in Medicine. ;ISSN: 0094-2405 ;EISSN: 2473-4209 ;DOI: 10.1002/mp.14896 ;PMID: 33894058

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