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Results 1 - 20 of 181,563  for All Library Resources

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1
GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables
GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables
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GRADE guidelines: 21 part 2. Test accuracy: inconsistency, imprecision, publication bias, and other domains for rating the certainty of evidence and presenting it in evidence profiles and summary of findings tables

Journal of clinical epidemiology, 2020-06, Vol.122, p.142-152 [Peer Reviewed Journal]

2020 Elsevier Inc. ;Copyright © 2020 Elsevier Inc. All rights reserved. ;2020. Elsevier Inc. ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2019.12.021 ;PMID: 32058069

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2
GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy
GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy
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GRADE guidelines: 21 part 1. Study design, risk of bias, and indirectness in rating the certainty across a body of evidence for test accuracy

Journal of clinical epidemiology, 2020-06, Vol.122, p.129-141 [Peer Reviewed Journal]

2020 Elsevier Inc. ;Copyright © 2020 Elsevier Inc. All rights reserved. ;2020. Elsevier Inc. ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2019.12.020 ;PMID: 32060007

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3
A Fast and Accurate Rule-Base Generation Method for Mamdani Fuzzy Systems
A Fast and Accurate Rule-Base Generation Method for Mamdani Fuzzy Systems
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A Fast and Accurate Rule-Base Generation Method for Mamdani Fuzzy Systems

IEEE transactions on fuzzy systems, 2018-04, p.715-733 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1063-6706 ;EISSN: 1941-0034 ;DOI: 10.1109/TFUZZ.2017.2688349

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4
Optimization design method of machine tool static geometric accuracy using tolerance modeling
Optimization design method of machine tool static geometric accuracy using tolerance modeling
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Optimization design method of machine tool static geometric accuracy using tolerance modeling

International journal of advanced manufacturing technology, 2022, Vol.118 (5-6), p.1793-1809 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021. ;ISSN: 0268-3768 ;EISSN: 1433-3015 ;DOI: 10.1007/s00170-021-07992-6

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5
Methods and reporting of systematic reviews of comparative accuracy were deficient: a methodological survey and proposed guidance
Methods and reporting of systematic reviews of comparative accuracy were deficient: a methodological survey and proposed guidance
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Methods and reporting of systematic reviews of comparative accuracy were deficient: a methodological survey and proposed guidance

Journal of clinical epidemiology, 2020-05, Vol.121, p.1-14 [Peer Reviewed Journal]

2020 ;Crown Copyright © 2020. Published by Elsevier Inc. All rights reserved. ;Copyright Elsevier Limited May 2020 ;Crown Copyright © 2020 Published by Elsevier Inc. 2020 ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2019.12.007 ;PMID: 31843693

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6
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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7
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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8
Protein structure accuracy estimation using geometry‐complete perceptron networks
Protein structure accuracy estimation using geometry‐complete perceptron networks
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Protein structure accuracy estimation using geometry‐complete perceptron networks

Protein science, 2024-03, Vol.33 (3), p.e4932-n/a [Peer Reviewed Journal]

2024 The Protein Society. ;2024 The Protein Society ;ISSN: 0961-8368 ;EISSN: 1469-896X ;DOI: 10.1002/pro.4932 ;PMID: 38380738

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9
Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed
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Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed

Journal of clinical epidemiology, 2024-01, Vol.165, p.111206-111206, Article 111206 [Peer Reviewed Journal]

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. ;2023. The Authors ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2023.10.022 ;PMID: 37925059

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10
Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine
Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine
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Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

BMC genomics, 2020-11, Vol.21 (1), p.796-796, Article 796 [Peer Reviewed Journal]

COPYRIGHT 2020 BioMed Central Ltd. ;COPYRIGHT 2020 BioMed Central Ltd. ;2020. 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) 2020 ;ISSN: 1471-2164 ;EISSN: 1471-2164 ;DOI: 10.1186/s12864-020-07188-4 ;PMID: 33198692

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11
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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12
Measures of Model Performance Based On the Log Accuracy Ratio
Measures of Model Performance Based On the Log Accuracy Ratio
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Measures of Model Performance Based On the Log Accuracy Ratio

Space Weather, 2018-01, Vol.16 (1), p.69-88 [Peer Reviewed Journal]

2018. American Geophysical Union. All Rights Reserved. ;ISSN: 1542-7390 ;ISSN: 1539-4964 ;EISSN: 1542-7390 ;DOI: 10.1002/2017SW001669

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13
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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14
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Proceedings of the National Academy of Sciences - PNAS, 2022-04, Vol.119 (15), p.e2113561119 [Peer Reviewed Journal]

Copyright National Academy of Sciences Apr 12, 2022 ;Copyright © 2022 the Author(s). Published by PNAS. 2022 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2113561119 ;PMID: 35394862

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15
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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16
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
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A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Sensors (Basel, Switzerland), 2017-02, Vol.17 (2), p.425-425 [Peer Reviewed Journal]

2017. 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. ;2017 by the authors. 2017 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s17020425 ;PMID: 28241451

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17
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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18
Fermionic neural-network states for ab-initio electronic structure
Fermionic neural-network states for ab-initio electronic structure
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Fermionic neural-network states for ab-initio electronic structure

Nature communications, 2020-05, Vol.11 (1), p.2368-2368, Article 2368 [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/s41467-020-15724-9 ;PMID: 32398658

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19
Slant-range accuracy assessment for the YaoGan 13
Slant-range accuracy assessment for the YaoGan 13
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Slant-range accuracy assessment for the YaoGan 13

Journal of engineering (Stevenage, England), 2019-10, Vol.2019 (19), p.5957-5961 [Peer Reviewed Journal]

2021 The Institution of Engineering and Technology ;ISSN: 2051-3305 ;EISSN: 2051-3305 ;DOI: 10.1049/joe.2019.0302

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20
Precision Stellar Characterization of FGKM Stars using an Empirical Spectral Library
Precision Stellar Characterization of FGKM Stars using an Empirical Spectral Library
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Precision Stellar Characterization of FGKM Stars using an Empirical Spectral Library

The Astrophysical journal, 2017-02, Vol.836 (1), p.77 [Peer Reviewed Journal]

2017. The American Astronomical Society. All rights reserved. ;Copyright IOP Publishing Feb 10, 2017 ;ISSN: 0004-637X ;EISSN: 1538-4357 ;DOI: 10.3847/1538-4357/836/1/77

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