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1
Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space
Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space
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Tracking the historical urban development by classifying Landsat MSS data with training samples migrated across time and space

International journal of digital earth, 2023-12, Vol.16 (1), p.2487-2502 [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: 1753-8947 ;EISSN: 1753-8955 ;DOI: 10.1080/17538947.2023.2230951

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2
A Methodology Combining Cosine Similarity with Classifier for Text Classification
A Methodology Combining Cosine Similarity with Classifier for Text Classification
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A Methodology Combining Cosine Similarity with Classifier for Text Classification

Applied artificial intelligence, 2020-04, Vol.34 (5), p.396-411 [Peer Reviewed Journal]

ISSN: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2020.1723868

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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
CLASSIFYING HUMANS: THE INDIRECT REVERSE OPERATIVITY OF MACHINE VISION
CLASSIFYING HUMANS: THE INDIRECT REVERSE OPERATIVITY OF MACHINE VISION
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CLASSIFYING HUMANS: THE INDIRECT REVERSE OPERATIVITY OF MACHINE VISION

Photographies, 2023-05, Vol.16 (2), p.263-289 [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;ISSN: 1754-0763 ;EISSN: 1754-0771 ;DOI: 10.1080/17540763.2023.2189160

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6
Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data
Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data
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Almond cultivar identification using machine learning classifiers applied to UAV-based multispectral data

International journal of remote sensing, 2023-03, Vol.44 (5), p.1533-1555 [Peer Reviewed Journal]

2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2023 ;ISSN: 0143-1161 ;EISSN: 1366-5901 ;DOI: 10.1080/01431161.2023.2185913

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7
A Homogeneous Ensemble Classifier for Breast Cancer Detection Using Parameters Tuning of MLP Neural Network
A Homogeneous Ensemble Classifier for Breast Cancer Detection Using Parameters Tuning of MLP Neural Network
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A Homogeneous Ensemble Classifier for Breast Cancer Detection Using Parameters Tuning of MLP Neural Network

Applied artificial intelligence, 2022-12, Vol.36 (1) [Peer Reviewed Journal]

2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 2022 ;2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 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: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2022.2031820

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8
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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9
Analysis of Birth Data using Ensemble Modeling Techniques
Analysis of Birth Data using Ensemble Modeling Techniques
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Analysis of Birth Data using Ensemble Modeling Techniques

Applied artificial intelligence, 2023-12, Vol.37 (1) [Peer Reviewed Journal]

2023 The Author(s). Published with license by Taylor & Francis Group, LLC. 2023 ;2023 The Author(s). Published with license by Taylor & Francis Group, LLC. 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: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2022.2158273

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10
An Intelligent Human Age and Gender Forecasting Framework Using Deep Learning Algorithms
An Intelligent Human Age and Gender Forecasting Framework Using Deep Learning Algorithms
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An Intelligent Human Age and Gender Forecasting Framework Using Deep Learning Algorithms

Applied artificial intelligence, 2022-12, Vol.36 (1) [Peer Reviewed Journal]

2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 2022 ;2022 The Author(s). Published with license by Taylor & Francis Group, LLC. 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: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2022.2073724

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11
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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12
Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance
Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance
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Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance

Applied artificial intelligence, 2021-05, Vol.35 (6), p.403-420 [Peer Reviewed Journal]

ISSN: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2021.1901032

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13
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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14
Indoor and Outdoor Classification Using Light Measurements and Machine Learning
Indoor and Outdoor Classification Using Light Measurements and Machine Learning
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Indoor and Outdoor Classification Using Light Measurements and Machine Learning

Applied artificial intelligence, 2022-12, Vol.36 (1) [Peer Reviewed Journal]

2021 The Author(s). Published with license by Taylor & Francis Group, LLC. 2021 ;2021 The Author(s). Published with license by Taylor & Francis Group, LLC. 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: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2021.2012001

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15
Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction
Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction
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Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction

Applied artificial intelligence, 2021-03, Vol.35 (4), p.290-303 [Peer Reviewed Journal]

ISSN: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2021.1877481

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16
Object-based image analysis of suburban landscapes using Landsat-8 imagery
Object-based image analysis of suburban landscapes using Landsat-8 imagery
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Object-based image analysis of suburban landscapes using Landsat-8 imagery

International journal of digital earth, 2019-06, Vol.12 (6), p.720-736 [Peer Reviewed Journal]

ISSN: 1753-8947 ;EISSN: 1753-8955 ;DOI: 10.1080/17538947.2018.1474959

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17
Bankruptcy Prediction Using Stacked Auto-Encoders
Bankruptcy Prediction Using Stacked Auto-Encoders
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Bankruptcy Prediction Using Stacked Auto-Encoders

Applied artificial intelligence, 2020-01, Vol.34 (1), p.80-100 [Peer Reviewed Journal]

ISSN: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2019.1691849

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18
Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature
Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature
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Crop classification based on the spectrotemporal signature derived from vegetation indices and accumulated temperature

International journal of digital earth, 2022-12, Vol.15 (1), p.626-652 [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 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: 1753-8947 ;EISSN: 1753-8955 ;DOI: 10.1080/17538947.2022.2036832

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19
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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20
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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