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1
Evaluation of electrical efficiency of photovoltaic thermal solar collector
Evaluation of electrical efficiency of photovoltaic thermal solar collector
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Evaluation of electrical efficiency of photovoltaic thermal solar collector

Engineering applications of computational fluid mechanics, 2020-01, Vol.14 (1), p.545-565 [Peer Reviewed Journal]

2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2020 ;2020 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: 1994-2060 ;EISSN: 1997-003X ;DOI: 10.1080/19942060.2020.1734094

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2
Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM
Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM
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Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM

The international journal of pavement engineering, 2022-07, Vol.23 (9), p.3274-3283 [Peer Reviewed Journal]

2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2021 ;ISSN: 1029-8436 ;EISSN: 1477-268X ;DOI: 10.1080/10298436.2021.1888092

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3
Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction
Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction
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Comparative analysis of classification techniques and input-output patterns for monthly rainfall prediction

Water Science, 2024-12, Vol.38 (1), p.192-208

EISSN: 2357-0008 ;DOI: 10.1080/23570008.2024.2323879

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4
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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5
A Model Using Support Vector Machines Recursive Feature Elimination (SVM-RFE) Algorithm to Classify Whether COPD Patients Have Been Continuously Managed According to GOLD Guidelines
A Model Using Support Vector Machines Recursive Feature Elimination (SVM-RFE) Algorithm to Classify Whether COPD Patients Have Been Continuously Managed According to GOLD Guidelines
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A Model Using Support Vector Machines Recursive Feature Elimination (SVM-RFE) Algorithm to Classify Whether COPD Patients Have Been Continuously Managed According to GOLD Guidelines

International journal of chronic obstructive pulmonary disease, 2020-01, Vol.15, p.2779-2786 [Peer Reviewed Journal]

COPYRIGHT 2020 Dove Medical Press Limited ;COPYRIGHT 2020 Dove Medical Press Limited ;2020. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Xia et al. 2020 Xia et al. ;ISSN: 1178-2005 ;ISSN: 1176-9106 ;EISSN: 1178-2005 ;DOI: 10.2147/COPD.S271237 ;PMID: 33177815

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6
Flood susceptibility mapping using meta-heuristic algorithms
Flood susceptibility mapping using meta-heuristic algorithms
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Flood susceptibility mapping using meta-heuristic algorithms

Geomatics, natural hazards and risk, 2022-12, Vol.13 (1), p.949-974 [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: 1947-5705 ;EISSN: 1947-5713 ;DOI: 10.1080/19475705.2022.2060138

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7
EEG signal classification of tinnitus based on SVM and sample entropy
EEG signal classification of tinnitus based on SVM and sample entropy
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EEG signal classification of tinnitus based on SVM and sample entropy

Computer methods in biomechanics and biomedical engineering, 2023-04, Vol.26 (5), p.580-594 [Peer Reviewed Journal]

2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2022 ;ISSN: 1025-5842 ;EISSN: 1476-8259 ;DOI: 10.1080/10255842.2022.2075698

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8
Support Vector Machine Method for Developing Ground Motion Models for Earthquakes in Western Part of China
Support Vector Machine Method for Developing Ground Motion Models for Earthquakes in Western Part of China
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Support Vector Machine Method for Developing Ground Motion Models for Earthquakes in Western Part of China

Journal of earthquake engineering : JEE, 2022-08, Vol.26 (11), p.5679-5694 [Peer Reviewed Journal]

2021 The Author(s). Published with license by Taylor & Francis Group, LLC. 2021 ;ISSN: 1363-2469 ;EISSN: 1559-808X ;DOI: 10.1080/13632469.2021.1884146

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9
Urban tree species classification using UAV-based multi-sensor data fusion and machine learning
Urban tree species classification using UAV-based multi-sensor data fusion and machine learning
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Urban tree species classification using UAV-based multi-sensor data fusion and machine learning

GIScience and remote sensing, 2021-11, Vol.58 (8), p.1250-1275 [Peer Reviewed Journal]

ISSN: 1548-1603 ;EISSN: 1943-7226 ;DOI: 10.1080/15481603.2021.1974275

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10
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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11
Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows
Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows
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Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Engineering applications of computational fluid mechanics, 2021-01, Vol.15 (1), p.1002-1015 [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: 1994-2060 ;EISSN: 1997-003X ;DOI: 10.1080/19942060.2021.1934546

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12
Machine Learning Analysis Classifies Patients with Primary Angle-Closure Glaucoma Using Abnormal Brain White Matter Function
Machine Learning Analysis Classifies Patients with Primary Angle-Closure Glaucoma Using Abnormal Brain White Matter Function
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Machine Learning Analysis Classifies Patients with Primary Angle-Closure Glaucoma Using Abnormal Brain White Matter Function

Clinical ophthalmology (Auckland, N.Z.), 2024-01, Vol.18, p.659-670 [Peer Reviewed Journal]

2024 Tang et al. ;COPYRIGHT 2024 Dove Medical Press Limited ;2024 Tang et al. 2024 Tang et al. ;ISSN: 1177-5467 ;ISSN: 1177-5483 ;EISSN: 1177-5483 ;DOI: 10.2147/OPTH.S451872 ;PMID: 38468914

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13
Safe transductive support vector machine
Safe transductive support vector machine
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Safe transductive support vector machine

Connection science, 2022-12, Vol.34 (1), p.942-959 [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: 0954-0091 ;EISSN: 1360-0494 ;DOI: 10.1080/09540091.2021.2024511

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14
Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey
Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey
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Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey

Journal of Taibah University for Science, 2017-05, Vol.11 (3), p.381-391 [Peer Reviewed Journal]

2016 The Authors ;ISSN: 1658-3655 ;EISSN: 1658-3655 ;DOI: 10.1016/j.jtusci.2016.04.005

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15
Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)
Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)
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Support vector machine regression (SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization (RRN)

Geo-spatial information science, 2020-07, Vol.23 (3), p.237-247 [Peer Reviewed Journal]

2020 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. 2020 ;2020 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: 10.1080/10095020.2020.1785958

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16
Applying Machine Learning Models to Predict Medication Nonadherence in Crohn's Disease Maintenance Therapy
Applying Machine Learning Models to Predict Medication Nonadherence in Crohn's Disease Maintenance Therapy
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Applying Machine Learning Models to Predict Medication Nonadherence in Crohn's Disease Maintenance Therapy

Patient preference and adherence, 2020-01, Vol.14, p.917-926 [Peer Reviewed Journal]

2020 Wang et al. ;2020. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Wang et al. 2020 Wang et al. ;ISSN: 1177-889X ;EISSN: 1177-889X ;DOI: 10.2147/PPA.S253732 ;PMID: 32581518

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17
HARNet: automatic recognition of human activity from mobile health data using CNN and transfer learning of LSTM with SVM
HARNet: automatic recognition of human activity from mobile health data using CNN and transfer learning of LSTM with SVM
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HARNet: automatic recognition of human activity from mobile health data using CNN and transfer learning of LSTM with SVM

Automatika, 2024-01, Vol.65 (1), p.167-178 [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 – 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 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2023.2290736

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18
Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data
Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data
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Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

International journal of digital earth, 2019-06, Vol.12 (6), p.661-678 [Peer Reviewed Journal]

ISSN: 1753-8947 ;EISSN: 1753-8955 ;DOI: 10.1080/17538947.2018.1471104

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19
Structural and functional brain abnormalities in drug-naive, first-episode, and chronic patients with schizophrenia: a multimodal MRI study
Structural and functional brain abnormalities in drug-naive, first-episode, and chronic patients with schizophrenia: a multimodal MRI study
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Structural and functional brain abnormalities in drug-naive, first-episode, and chronic patients with schizophrenia: a multimodal MRI study

Neuropsychiatric disease and treatment, 2018-01, Vol.14, p.2889-2904 [Peer Reviewed Journal]

COPYRIGHT 2018 Dove Medical Press Limited ;COPYRIGHT 2018 Dove Medical Press Limited ;2018. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018 Wu et al. This work is published and licensed by Dove Medical Press Limited 2018 ;ISSN: 1176-6328 ;ISSN: 1178-2021 ;EISSN: 1178-2021 ;DOI: 10.2147/NDT.S174356 ;PMID: 30464473

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20
Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach
Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach
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Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach

Neuropsychiatric disease and treatment, 2020-01, Vol.16, p.691-702 [Peer Reviewed Journal]

2020 Sun et al. ;COPYRIGHT 2020 Dove Medical Press Limited ;COPYRIGHT 2020 Dove Medical Press Limited ;2020. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 Sun et al. 2020 Sun et al. ;ISSN: 1176-6328 ;ISSN: 1178-2021 ;EISSN: 1178-2021 ;DOI: 10.2147/NDT.S239013 ;PMID: 32210565

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