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1
Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest
Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest
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Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

Geo-spatial information science, 2023-07, Vol.26 (3), p.302-320 [Peer Reviewed Journal]

2022 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. 2022 ;2022 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 1009-5020 ;EISSN: 1993-5153 ;DOI: 10.1080/10095020.2022.2100287

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2
Forecasting failure-prone air pressure systems (FFAPS) in vehicles using machine learning
Forecasting failure-prone air pressure systems (FFAPS) in vehicles using machine learning
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Forecasting failure-prone air pressure systems (FFAPS) in vehicles using machine learning

Automatika, 2024, Vol.65 (1), p.1-13 [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 0005-1144 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2023.2269514

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3
Monitoring the condition of nitrogen-filled tires using weightless neural networks
Monitoring the condition of nitrogen-filled tires using weightless neural networks
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Monitoring the condition of nitrogen-filled tires using weightless neural networks

Automatika, 2024-04, Vol.65 (2), p.523-537 [Peer Reviewed Journal]

2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0005-1144 ;ISSN: 1848-3380 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2024.2310979

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4
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images
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Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

European journal of remote sensing, 2017-01, Vol.50 (1), p.144-154 [Peer Reviewed Journal]

2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2017 ;2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 2279-7254 ;EISSN: 2279-7254 ;DOI: 10.1080/22797254.2017.1299557

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5
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
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A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China

Geomatics, natural hazards and risk, 2017-12, Vol.8 (2), p.1955-1977 [Peer Reviewed Journal]

2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2017 ;2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 1947-5705 ;EISSN: 1947-5713 ;DOI: 10.1080/19475705.2017.1401560

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6
A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques
A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques
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A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques

Cogent food & agriculture, 2024-12, Vol.10 (1) [Peer Reviewed Journal]

EISSN: 2331-1932 ;DOI: 10.1080/23311932.2024.2339572

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7
A novel approach to predict competency and the hidden risk factor by using various machine learning classifiers
A novel approach to predict competency and the hidden risk factor by using various machine learning classifiers
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A novel approach to predict competency and the hidden risk factor by using various machine learning classifiers

Automatika, 2023-07, Vol.64 (3), p.550-564 [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023 ;2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 0005-1144 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2023.2200347

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8
Generalized Bayes Quantification Learning under Dataset Shift
Generalized Bayes Quantification Learning under Dataset Shift
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Generalized Bayes Quantification Learning under Dataset Shift

Journal of the American Statistical Association, 2022-10, Vol.117 (540), p.2163-2181 [Peer Reviewed Journal]

2021 The Author(s). Published with license by Taylor & Francis Group, LLC. 2021 ;ISSN: 0162-1459 ;EISSN: 1537-274X ;DOI: 10.1080/01621459.2021.1909599

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9
A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping
A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping
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A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Geomatics, natural hazards and risk, 2021-01, Vol.12 (1), p.1741-1777 [Peer Reviewed Journal]

2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2021 ;2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 1947-5705 ;ISSN: 1947-5713 ;EISSN: 1947-5713 ;DOI: 10.1080/19475705.2021.1944330

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10
An efficient method for bearing fault diagnosis
An efficient method for bearing fault diagnosis
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An efficient method for bearing fault diagnosis

Systems science & control engineering, 2024-12, Vol.12 (1) [Peer Reviewed Journal]

EISSN: 2164-2583 ;DOI: 10.1080/21642583.2024.2329264

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11
Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery
Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery
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Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery

European journal of remote sensing, 2017-01, Vol.50 (1), p.564-576 [Peer Reviewed Journal]

2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2017 ;2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 2279-7254 ;EISSN: 2279-7254 ;DOI: 10.1080/22797254.2017.1373602

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12
A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets
A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets
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A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets

Cogent engineering, 2023-12, Vol.10 (1) [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;ISSN: 2331-1916 ;EISSN: 2331-1916 ;DOI: 10.1080/23311916.2023.2232602

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13
A comparison of machine learning models for suspended sediment load classification
A comparison of machine learning models for suspended sediment load classification
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A comparison of machine learning models for suspended sediment load classification

Engineering applications of computational fluid mechanics, 2022-12, Vol.16 (1), p.1211-1232 [Peer Reviewed Journal]

2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2022 ;2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1994-2060 ;EISSN: 1997-003X ;DOI: 10.1080/19942060.2022.2073565

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14
An ensemble machine learning approach for classification tasks using feature generation
An ensemble machine learning approach for classification tasks using feature generation
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An ensemble machine learning approach for classification tasks using feature generation

Connection science, 2023-12, Vol.35 (1) [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution – Non-Commercial License http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0954-0091 ;EISSN: 1360-0494 ;DOI: 10.1080/09540091.2023.2231168

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15
Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools
Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools
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Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

Science and technology of advanced materials, 2019-12, Vol.20 (1), p.532-542 [Peer Reviewed Journal]

2019 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. 2019 ;2019 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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. ;2019 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group. 2019 The Author(s) ;ISSN: 1468-6996 ;EISSN: 1878-5514 ;DOI: 10.1080/14686996.2019.1610668 ;PMID: 31231445

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16
A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS
A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS
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A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS

Geomatics, natural hazards and risk, 2017-12, Vol.8 (2), p.649-671 [Peer Reviewed Journal]

2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2016 ;2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 1947-5705 ;EISSN: 1947-5713 ;DOI: 10.1080/19475705.2016.1255667

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17
Ensemble Feature Selection Framework for Paddy Yield Prediction in Cauvery Basin using Machine Learning Classifiers
Ensemble Feature Selection Framework for Paddy Yield Prediction in Cauvery Basin using Machine Learning Classifiers
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Ensemble Feature Selection Framework for Paddy Yield Prediction in Cauvery Basin using Machine Learning Classifiers

Cogent engineering, 2023-12, Vol.10 (2) [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 2331-1916 ;EISSN: 2331-1916 ;DOI: 10.1080/23311916.2023.2250061

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18
Object-based classification of hyperspectral data using Random Forest algorithm
Object-based classification of hyperspectral data using Random Forest algorithm
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Object-based classification of hyperspectral data using Random Forest algorithm

Geo-spatial information science, 2018-04, Vol.21 (2), p.127-138 [Peer Reviewed Journal]

2018 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 1009-5020 ;EISSN: 1993-5153 ;DOI: 10.1080/10095020.2017.1399674

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19
Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach
Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach
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Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach

European journal of remote sensing, 2023-12, Vol.56 (1) [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 2279-7254 ;EISSN: 2279-7254 ;DOI: 10.1080/22797254.2023.2173659

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20
Winter remote sensing images are more suitable for forest mapping in Jiangxi Province
Winter remote sensing images are more suitable for forest mapping in Jiangxi Province
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Winter remote sensing images are more suitable for forest mapping in Jiangxi Province

European journal of remote sensing, 2023-12, Vol.56 (1) [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License 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: 2279-7254 ;EISSN: 2279-7254 ;DOI: 10.1080/22797254.2023.2237655

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