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1
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
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The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Computer graphics forum, 2020-06, Vol.39 (3), p.713-756 [Peer Reviewed Journal]

Attribution ;ISSN: 0167-7055 ;EISSN: 1467-8659 ;DOI: 10.1111/cgf.14034

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2
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

IEEE transaction on neural networks and learning systems, 2021-11, Vol.32 (11), p.4793-4813

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2020.3027314 ;PMID: 33079674 ;CODEN: ITNNAL

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3
CC2Vec: Distributed Representations of Code Changes
CC2Vec: Distributed Representations of Code Changes
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CC2Vec: Distributed Representations of Code Changes

Distributed under a Creative Commons Attribution 4.0 International License ;DOI: 10.1145/3377811.3380361

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4
Correction: Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training
Correction: Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training
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Correction: Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PloS one, 2023-10, Vol.18 (10), p.e0292513-e0292513 [Peer Reviewed Journal]

COPYRIGHT 2023 Public Library of Science ;2023 Bi 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. ;2023 Bi et al 2023 Bi et al ;2023 Bi 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. ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0292513

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5
A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO[sub.2] Concentration as an Example
A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO[sub.2] Concentration as an Example
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A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO[sub.2] Concentration as an Example

Sensors (Basel, Switzerland), 2023-12, Vol.24 (1) [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s24010244

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6
Response to "Comment on 'Deep Ensemble Machine Learning Framework for the Estimation of PM 2.5 Concentrations'"
Response to "Comment on 'Deep Ensemble Machine Learning Framework for the Estimation of PM 2.5 Concentrations'"
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Response to "Comment on 'Deep Ensemble Machine Learning Framework for the Estimation of PM 2.5 Concentrations'"

Environmental health perspectives, 2022-06, Vol.130 (6), p.68002 [Peer Reviewed Journal]

EISSN: 1552-9924 ;DOI: 10.1289/EHP11438 ;PMID: 35652827

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7
Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
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Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

Water resources research, 2020-05, Vol.56 (5), p.n/a [Peer Reviewed Journal]

2020. American Geophysical Union. All Rights Reserved. ;ISSN: 0043-1397 ;EISSN: 1944-7973 ;DOI: 10.1029/2019WR026731

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8
Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods
Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods
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Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods

American journal of roentgenology (1976), 2019-01, Vol.212 (1), p.38-43 [Peer Reviewed Journal]

ISSN: 0361-803X ;EISSN: 1546-3141 ;DOI: 10.2214/AJR.18.20224 ;PMID: 30332290

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9
Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong Kong
Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong Kong
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Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong Kong

BMC medical research methodology, 2023-02, Vol.23 (1), p.38-38, Article 38 [Peer Reviewed Journal]

COPYRIGHT 2023 BioMed Central Ltd. ;2023. 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) 2023 ;ISSN: 1471-2288 ;EISSN: 1471-2288 ;DOI: 10.1186/s12874-023-01851-6 ;PMID: 36782144

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10
Mastering the game of Go without human knowledge
Mastering the game of Go without human knowledge
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Mastering the game of Go without human knowledge

Nature (London), 2017-10, Vol.550 (7676), p.354-359 [Peer Reviewed Journal]

COPYRIGHT 2017 Nature Publishing Group ;COPYRIGHT 2017 Nature Publishing Group ;Copyright Nature Publishing Group Oct 19, 2017 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/nature24270 ;PMID: 29052630

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11
Correction to "Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population"
Correction to "Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population"
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Correction to "Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population"

The journal of clinical endocrinology and metabolism, 2023-01, Vol.108 (2), p.e29-e29 [Peer Reviewed Journal]

COPYRIGHT 2023 Oxford University Press ;ISSN: 0021-972X ;EISSN: 1945-7197 ;DOI: 10.1210/clinem/dgac676 ;PMID: 36451340

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12
This AI researcher is trying to ward off a reproducibility crisis
This AI researcher is trying to ward off a reproducibility crisis
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This AI researcher is trying to ward off a reproducibility crisis

Nature, 2020-01, Vol.577 (7788), p.14-14 [Peer Reviewed Journal]

ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/d41586-019-03895-5 ;PMID: 31871325

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13
Correction: The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice
Correction: The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice
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Correction: The path to international medals: A supervised machine learning approach to explore the impact of coach-led sport-specific and non-specific practice

PloS one, 2020-12, Vol.15 (12), p.e0244509-e0244509 [Peer Reviewed Journal]

COPYRIGHT 2020 Public Library of Science ;COPYRIGHT 2020 Public Library of Science ;2020 Barth 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. ;2020 Barth et al 2020 Barth et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0244509 ;PMID: 33338055

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14
Deep learning: new computational modelling techniques for genomics
Deep learning: new computational modelling techniques for genomics
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Deep learning: new computational modelling techniques for genomics

Nature reviews. Genetics, 2019-07, Vol.20 (7), p.389-403 [Peer Reviewed Journal]

COPYRIGHT 2019 Nature Publishing Group ;COPYRIGHT 2019 Nature Publishing Group ;Springer Nature Limited 2019. ;ISSN: 1471-0056 ;EISSN: 1471-0064 ;DOI: 10.1038/s41576-019-0122-6 ;PMID: 30971806

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15
How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM[sub.2.5] Using Machine Learning?
How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM[sub.2.5] Using Machine Learning?
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How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM[sub.2.5] Using Machine Learning?

Remote sensing (Basel, Switzerland), 2023-07, Vol.15 (15) [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs15153780

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16
Do no harm: a roadmap for responsible machine learning for health care
Do no harm: a roadmap for responsible machine learning for health care
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Article 		Add to My Research

Do no harm: a roadmap for responsible machine learning for health care

Nature medicine, 2019-09, Vol.25 (9), p.1337-1340 [Peer Reviewed Journal]

COPYRIGHT 2019 Nature Publishing Group ;COPYRIGHT 2019 Nature Publishing Group ;Copyright Nature Publishing Group Sep 2019 ;ISSN: 1078-8956 ;EISSN: 1546-170X ;DOI: 10.1038/s41591-019-0548-6 ;PMID: 31427808

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17
SVF-Net: Learning Deformable Image Registration Using Shape Matching
SVF-Net: Learning Deformable Image Registration Using Shape Matching
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Conference Proceeding 		Add to My Research

SVF-Net: Learning Deformable Image Registration Using Shape Matching

Distributed under a Creative Commons Attribution 4.0 International License ;DOI: 10.1007/978-3-319-66182-7_31

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18
Machine Learning in Psychometrics and Psychological Research
Machine Learning in Psychometrics and Psychological Research
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Article 		Add to My Research

Machine Learning in Psychometrics and Psychological Research

Frontiers in psychology, 2020-01, Vol.10, p.2970-2970 [Peer Reviewed Journal]

Copyright © 2020 Orrù, Monaro, Conversano, Gemignani and Sartori. ;Copyright © 2020 Orrù, Monaro, Conversano, Gemignani and Sartori. 2020 Orrù, Monaro, Conversano, Gemignani and Sartori ;ISSN: 1664-1078 ;EISSN: 1664-1078 ;DOI: 10.3389/fpsyg.2019.02970 ;PMID: 31998200

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19
Retracted: Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing
Retracted: Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing
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Retracted: Implementing Machine Learning for Supply-Demand Shifts and Price Impacts in Farmer Market for Tool and Equipment Sharing

Journal of food quality, 2024-01, Vol.2024, p.1-1 [Peer Reviewed Journal]

Copyright © 2024 Journal of Food Quality. ;COPYRIGHT 2024 Hindawi Limited ;Copyright © 2024 Journal of Food Quality. 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: 0146-9428 ;EISSN: 1745-4557 ;DOI: 10.1155/2024/9826861

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20
0062 Improved Circadian Data Ordering in the Presence of Biological and Technical Confounds
0062 Improved Circadian Data Ordering in the Presence of Biological and Technical Confounds
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0062 Improved Circadian Data Ordering in the Presence of Biological and Technical Confounds

Sleep (New York, N.Y.), 2020-05, Vol.43 (Supplement_1), p.A24-A26 [Peer Reviewed Journal]

Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com. 2020 ;Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com. ;ISSN: 0161-8105 ;EISSN: 1550-9109 ;DOI: 10.1093/sleep/zsaa056.060

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