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1
Prediction of heart disease and classifiers’ sensitivity analysis
Prediction of heart disease and classifiers’ sensitivity analysis
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Prediction of heart disease and classifiers’ sensitivity analysis

BMC bioinformatics, 2020-07, Vol.21 (1), p.1-278, Article 278 [Peer Reviewed Journal]

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: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-020-03626-y ;PMID: 32615980

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2
Sex‐related patterns in the electroencephalogram and their relevance in machine learning classifiers
Sex‐related patterns in the electroencephalogram and their relevance in machine learning classifiers
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Sex‐related patterns in the electroencephalogram and their relevance in machine learning classifiers

Human brain mapping, 2023-10, Vol.44 (14), p.4848-4858 [Peer Reviewed Journal]

2023 The Authors. published by Wiley Periodicals LLC. ;2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. ;2023. 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: 1065-9471 ;EISSN: 1097-0193 ;DOI: 10.1002/hbm.26417 ;PMID: 37461294

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3
Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification
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Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

Remote sensing (Basel, Switzerland), 2019-07, Vol.11 (14), p.1713 [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/rs11141713

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4
Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images
Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images
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Comparison of Random Forest and Support Vector Machine Classifiers for Regional Land Cover Mapping Using Coarse Resolution FY-3C Images

Remote sensing (Basel, Switzerland), 2022-02, Vol.14 (3), p.574 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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/rs14030574

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5
SAR Oil Spill Detection System through Random Forest Classifiers
SAR Oil Spill Detection System through Random Forest Classifiers
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SAR Oil Spill Detection System through Random Forest Classifiers

Remote sensing (Basel, Switzerland), 2021-06, Vol.13 (11), p.2044 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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/rs13112044

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6
Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression
Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression
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Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression

Journal of clinical epidemiology, 2010-08, Vol.63 (8), p.826-833 [Peer Reviewed Journal]

Elsevier Inc. ;2010 Elsevier Inc. ;2015 INIST-CNRS ;Copyright (c) 2010 Elsevier Inc. All rights reserved. ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2009.11.020 ;PMID: 20630332

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7
Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis
Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis
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Using Voting-Based Ensemble Classifiers to Map Invasive Phragmites australis

Remote sensing (Basel, Switzerland), 2023-07, Vol.15 (14), p.3511 [Peer Reviewed Journal]

2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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/rs15143511

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8
Word2Vec inversion and traditional text classifiers for phenotyping lupus
Word2Vec inversion and traditional text classifiers for phenotyping lupus
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Word2Vec inversion and traditional text classifiers for phenotyping lupus

BMC medical informatics and decision making, 2017-08, Vol.17 (1), p.126-126, Article 126 [Peer Reviewed Journal]

COPYRIGHT 2017 BioMed Central Ltd. ;Copyright BioMed Central 2017 ;The Author(s) 2017 ;ISSN: 1472-6947 ;EISSN: 1472-6947 ;DOI: 10.1186/s12911-017-0518-1 ;PMID: 28830409

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9
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
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Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

Remote sensing (Basel, Switzerland), 2019-06, Vol.11 (11), p.1380 [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/rs11111380

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10
Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures
Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures
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Multi-Image-Feature-Based Hierarchical Concrete Crack Identification Framework Using Optimized SVM Multi-Classifiers and D–S Fusion Algorithm for Bridge Structures

Remote sensing (Basel, Switzerland), 2021-01, Vol.13 (2), p.240 [Peer Reviewed Journal]

2021. 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. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13020240

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11
Neural Classifiers with Limited Connectivity and Recurrent Readouts
Neural Classifiers with Limited Connectivity and Recurrent Readouts
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Neural Classifiers with Limited Connectivity and Recurrent Readouts

The Journal of neuroscience, 2018-11, Vol.38 (46), p.9900-9924 [Peer Reviewed Journal]

Copyright © 2018 the authors 0270-6474/18/389900-25$15.00/0. ;Copyright Society for Neuroscience Nov 14, 2018 ;Copyright © 2018 the authors 0270-6474/18/389900-25$15.00/0 2018 ;ISSN: 0270-6474 ;EISSN: 1529-2401 ;DOI: 10.1523/JNEUROSCI.3506-17.2018 ;PMID: 30249794

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12
Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection
Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection
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Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection

Computers in biology and medicine, 2021-08, Vol.135, p.104585-104585, Article 104585 [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.2021.104585 ;PMID: 34229144

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13
Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis
Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis
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Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis

Remote sensing (Basel, Switzerland), 2019-02, Vol.11 (3), p.359 [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/rs11030359

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14
A rapid screening classifier for diagnosing COVID-19
A rapid screening classifier for diagnosing COVID-19
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A rapid screening classifier for diagnosing COVID-19

International journal of biological sciences, 2021-01, Vol.17 (2), p.539-548 [Peer Reviewed Journal]

The author(s). ;2021. This work is published 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. ;The author(s) 2021 ;ISSN: 1449-2288 ;EISSN: 1449-2288 ;DOI: 10.7150/ijbs.53982 ;PMID: 33613111

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15
Artificial neural network classifier predicts neuroblastoma patients' outcome
Artificial neural network classifier predicts neuroblastoma patients' outcome
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Artificial neural network classifier predicts neuroblastoma patients' outcome

BMC bioinformatics, 2016-11, Vol.17 (Suppl 12), p.347-347, Article 347 [Peer Reviewed Journal]

COPYRIGHT 2016 BioMed Central Ltd. ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-016-1194-3 ;PMID: 28185577

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16
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
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A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

Remote sensing (Basel, Switzerland), 2019-10, Vol.11 (19), p.2326 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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/rs11192326

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17
Adaptive neural network classifier for decoding MEG signals
Adaptive neural network classifier for decoding MEG signals
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Adaptive neural network classifier for decoding MEG signals

NeuroImage (Orlando, Fla.), 2019-08, Vol.197, p.425-434 [Peer Reviewed Journal]

2019 The Authors ;Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved. ;2019. The Authors ;2019 The Authors 2019 ;ISSN: 1053-8119 ;EISSN: 1095-9572 ;DOI: 10.1016/j.neuroimage.2019.04.068 ;PMID: 31059799

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18
MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder
MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder
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MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder

BioData mining, 2024-03, Vol.17 (1), p.9-9 [Peer Reviewed Journal]

2024. The Author(s). ;COPYRIGHT 2024 BioMed Central Ltd. ;2024. 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) 2024 ;ISSN: 1756-0381 ;EISSN: 1756-0381 ;DOI: 10.1186/s13040-024-00360-6 ;PMID: 38444019

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19
Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers
Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers
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Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (1), p.177 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 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/rs12010177

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20
Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study
Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study
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Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study

BMC medicine, 2021-03, Vol.19 (1), p.80-80, Article 80 [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: 1741-7015 ;EISSN: 1741-7015 ;DOI: 10.1186/s12916-021-01953-2 ;PMID: 33775248

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