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1
Performance analysis of support vector machines classifiers in breast cancer mammography recognition
Performance analysis of support vector machines classifiers in breast cancer mammography recognition
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Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Neural computing & applications, 2014-04, Vol.24 (5), p.1163-1177 [Peer Reviewed Journal]

Springer-Verlag London 2013 ;2015 INIST-CNRS ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-012-1324-4

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2
COVID-19 cough classification using machine learning and global smartphone recordings
COVID-19 cough classification using machine learning and global smartphone recordings
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COVID-19 cough classification using machine learning and global smartphone recordings

Computers in biology and medicine, 2021-08, Vol.135, p.104572-104572, Article 104572 [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.2021.104572 ;PMID: 34182331

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3
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
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Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

Sensors (Basel, Switzerland), 2017-12, Vol.18 (1), p.18 [Peer Reviewed Journal]

2018. 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/s18010018 ;PMID: 29271909

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4
Comparing different supervised machine learning algorithms for disease prediction
Comparing different supervised machine learning algorithms for disease prediction
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Comparing different supervised machine learning algorithms for disease prediction

BMC medical informatics and decision making, 2019-12, Vol.19 (1), p.281-281, Article 281 [Peer Reviewed Journal]

COPYRIGHT 2019 BioMed Central Ltd. ;2019. 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). 2019 ;ISSN: 1472-6947 ;EISSN: 1472-6947 ;DOI: 10.1186/s12911-019-1004-8 ;PMID: 31864346

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5
Selecting training sets for support vector machines: a review
Selecting training sets for support vector machines: a review
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Selecting training sets for support vector machines: a review

The Artificial intelligence review, 2019-08, Vol.52 (2), p.857-900 [Peer Reviewed Journal]

The Author(s) 2018 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2018). All Rights Reserved. © 2018. 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: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-017-9611-1

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6
Exploiting machine learning for end-to-end drug discovery and development
Exploiting machine learning for end-to-end drug discovery and development
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Exploiting machine learning for end-to-end drug discovery and development

Nature materials, 2019-05, Vol.18 (5), p.435-441 [Peer Reviewed Journal]

Springer Nature Limited 2019. ;ISSN: 1476-1122 ;EISSN: 1476-4660 ;DOI: 10.1038/s41563-019-0338-z ;PMID: 31000803

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7
Support vector machines based non-contact fault diagnosis system for bearings
Support vector machines based non-contact fault diagnosis system for bearings
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Support vector machines based non-contact fault diagnosis system for bearings

Journal of intelligent manufacturing, 2020-06, Vol.31 (5), p.1275-1289 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Springer Science+Business Media, LLC, part of Springer Nature 2019. ;ISSN: 0956-5515 ;EISSN: 1572-8145 ;DOI: 10.1007/s10845-019-01511-x

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8
Using machine learning to predict student difficulties from learning session data
Using machine learning to predict student difficulties from learning session data
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Using machine learning to predict student difficulties from learning session data

The Artificial intelligence review, 2019-06, Vol.52 (1), p.381-407 [Peer Reviewed Journal]

Springer Science+Business Media B.V., part of Springer Nature 2018 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-018-9620-8

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9
Support vector machine for modelling and simulation of heat exchangers
Support vector machine for modelling and simulation of heat exchangers
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Support vector machine for modelling and simulation of heat exchangers

Thermal science, 2020, Vol.24 (1 Part B), p.499-503 [Peer Reviewed Journal]

2020. This work is licensed under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0354-9836 ;EISSN: 2334-7163 ;DOI: 10.2298/TSCI190419398M

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10
Comparative evaluation of machine learning models for groundwater quality assessment
Comparative evaluation of machine learning models for groundwater quality assessment
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Comparative evaluation of machine learning models for groundwater quality assessment

Environmental monitoring and assessment, 2020-12, Vol.192 (12), p.776, Article 776 [Peer Reviewed Journal]

Springer Nature Switzerland AG 2020 ;Springer Nature Switzerland AG 2020. ;ISSN: 0167-6369 ;EISSN: 1573-2959 ;DOI: 10.1007/s10661-020-08695-3 ;PMID: 33219864

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11
A review on multi-class TWSVM
A review on multi-class TWSVM
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A review on multi-class TWSVM

The Artificial intelligence review, 2019-08, Vol.52 (2), p.775-801 [Peer Reviewed Journal]

Springer Science+Business Media B.V. 2017 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2017). All Rights Reserved. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-017-9586-y

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12
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
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COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Computers in biology and medicine, 2020-06, Vol.121, p.103805-103805, Article 103805 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;2020 Elsevier Ltd. All rights reserved. 2020 Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103805 ;PMID: 32568679

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13
Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
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Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

Human brain mapping, 2013-12, Vol.34 (12), p.3411-3425 [Peer Reviewed Journal]

Copyright © 2012 Wiley Periodicals, Inc. ;2014 INIST-CNRS ;ISSN: 1065-9471 ;EISSN: 1097-0193 ;DOI: 10.1002/hbm.22156 ;PMID: 22927119

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14
Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning
Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning
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Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning

Sensors (Basel, Switzerland), 2019-03, Vol.19 (6), p.1284 [Peer Reviewed Journal]

2019 by the authors. 2019 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s19061284 ;PMID: 30875732

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15
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
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SVM-RFE: selection and visualization of the most relevant features through non-linear kernels

BMC bioinformatics, 2018-11, Vol.19 (1), p.432-432, Article 432 [Peer Reviewed Journal]

COPYRIGHT 2018 BioMed Central Ltd. ;cc-by (c) Sanz, Hector et al., 2018 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/es ;The Author(s). 2018 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-018-2451-4 ;PMID: 30453885

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16
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
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Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

PloS one, 2017-09, Vol.12 (9), p.e0184129-e0184129 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Chen 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. ;2017 Chen et al 2017 Chen et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0184129 ;PMID: 28873455

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17
Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm
Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm
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Diagnosis by Volatile Organic Compounds in Exhaled Breath from Lung Cancer Patients Using Support Vector Machine Algorithm

Sensors (Basel, Switzerland), 2017-02, Vol.17 (2), p.287-287 [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/s17020287 ;PMID: 28165388

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18
Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling
Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling
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Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling

Archives of toxicology, 2021-05, Vol.95 (5), p.1683-1701 [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: 0340-5761 ;EISSN: 1432-0738 ;DOI: 10.1007/s00204-021-03023-1 ;PMID: 33713150

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19
Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study
Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study
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Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

Scientific reports, 2020-10, Vol.10 (1), p.18716-18716, Article 18716 [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: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-020-75767-2 ;PMID: 33127965

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20
Extreme learning machine-based classification of ADHD using brain structural MRI data
Extreme learning machine-based classification of ADHD using brain structural MRI data
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Extreme learning machine-based classification of ADHD using brain structural MRI data

PloS one, 2013-11, Vol.8 (11), p.e79476-e79476 [Peer Reviewed Journal]

COPYRIGHT 2013 Public Library of Science ;COPYRIGHT 2013 Public Library of Science ;2013 Peng et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/3.0/ (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. ;2013 Peng et al 2013 Peng et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0079476 ;PMID: 24260229

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