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1
Phishing Detection System through Hybrid Machine Learning Based on URL
Phishing Detection System through Hybrid Machine Learning Based on URL
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Phishing Detection System through Hybrid Machine Learning Based on URL

IEEE access, 2023-01, Vol.11, p.1-1 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2023.3252366 ;CODEN: IAECCG

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2
Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors
Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors
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Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

Sensors (Basel, Switzerland), 2017-05, Vol.17 (6), p.1229 [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/s17061229 ;PMID: 28555016

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3
A comprehensive evaluation of ensemble learning for stock-market prediction
A comprehensive evaluation of ensemble learning for stock-market prediction
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A comprehensive evaluation of ensemble learning for stock-market prediction

Journal of big data, 2020-03, Vol.7 (1), p.1-40, Article 20 [Peer Reviewed Journal]

The Author(s) 2020 ;Journal of Big Data is a copyright of Springer, (2020). All Rights Reserved. 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: 2196-1115 ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-020-00299-5

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4
Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability
Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability
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Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability

Sensors (Basel, Switzerland), 2022-04, Vol.22 (9), p.3314 [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. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22093314 ;PMID: 35591006

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5
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis
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Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

Sensors (Basel, Switzerland), 2022-10, Vol.22 (19), p.7596 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;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. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22197596 ;PMID: 36236694

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6
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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7
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
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Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric

PloS one, 2017-06, Vol.12 (6), p.e0177678-e0177678 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Boughorbel 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 Boughorbel et al 2017 Boughorbel et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0177678 ;PMID: 28574989

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8
Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV
Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV
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Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV

IEEE access, 2022, Vol.10, p.80151-80173 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2022.3165792 ;CODEN: IAECCG

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9
Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data
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Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Sensors (Basel, Switzerland), 2022-11, Vol.22 (23), p.9323 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;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. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22239323 ;PMID: 36502025

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10
"What is relevant in a text document?": An interpretable machine learning approach
"What is relevant in a text document?": An interpretable machine learning approach
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"What is relevant in a text document?": An interpretable machine learning approach

PloS one, 2017-08, Vol.12 (8), p.e0181142-e0181142 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Arras 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 Arras et al 2017 Arras et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0181142 ;PMID: 28800619

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11
A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier
A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier
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A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

Energies (Basel), 2016-10, Vol.9 (10), p.778-778 [Peer Reviewed Journal]

Copyright MDPI AG 2016 ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en9100778

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12
Machine learning, medical diagnosis, and biomedical engineering research - commentary
Machine learning, medical diagnosis, and biomedical engineering research - commentary
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Machine learning, medical diagnosis, and biomedical engineering research - commentary

Biomedical engineering online, 2014-07, Vol.13 (1), p.94-94 [Peer Reviewed Journal]

COPYRIGHT 2014 BioMed Central Ltd. ;2014 Foster et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. ;Copyright © 2014 Foster et al.; licensee BioMed Central Ltd. 2014 Foster et al.; licensee BioMed Central Ltd. ;ISSN: 1475-925X ;EISSN: 1475-925X ;DOI: 10.1186/1475-925X-13-94 ;PMID: 24998888

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13
SVM and SVM Ensembles in Breast Cancer Prediction
SVM and SVM Ensembles in Breast Cancer Prediction
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SVM and SVM Ensembles in Breast Cancer Prediction

PloS one, 2017-01, Vol.12 (1), p.e0161501-e0161501 [Peer Reviewed Journal]

COPYRIGHT 2017 Public Library of Science ;COPYRIGHT 2017 Public Library of Science ;2017 Huang 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 Huang et al 2017 Huang et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0161501 ;PMID: 28060807

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14
Analysis and prediction of human acetylation using a cascade classifier based on support vector machine
Analysis and prediction of human acetylation using a cascade classifier based on support vector machine
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Analysis and prediction of human acetylation using a cascade classifier based on support vector machine

BMC bioinformatics, 2019-06, Vol.20 (1), p.346-346, Article 346 [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: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-019-2938-7 ;PMID: 31208321

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15
CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques
CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques
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CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques

BMC bioinformatics, 2021-04, Vol.22 (1), p.216-216, Article 216 [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: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-04134-3 ;PMID: 33902446

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16
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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17
A systematic comparison of supervised classifiers
A systematic comparison of supervised classifiers
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A systematic comparison of supervised classifiers

PloS one, 2014-04, Vol.9 (4), p.e94137-e94137 [Peer Reviewed Journal]

COPYRIGHT 2014 Public Library of Science ;COPYRIGHT 2014 Public Library of Science ;2014 Amancio 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. ;2014 Amancio et al 2014 Amancio et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0094137 ;PMID: 24763312

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18
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data
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An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

Sensors (Basel, Switzerland), 2020-09, Vol.20 (19), p.5564 [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. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20195564 ;PMID: 32998427

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19
Software Defect Prediction Using an Intelligent Ensemble-Based Model
Software Defect Prediction Using an Intelligent Ensemble-Based Model
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Software Defect Prediction Using an Intelligent Ensemble-Based Model

IEEE access, 2024, Vol.12, p.20376-20395 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2024.3358201 ;CODEN: IAECCG

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20
Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle
Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle
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Investigating Performance Trends of Simulated Real-time Solar Flare Predictions: The Impacts of Training Windows, Data Volumes, and the Solar Cycle

The Astrophysical journal, 2024-04, Vol.964 (2), p.163 [Peer Reviewed Journal]

2024. The Author(s). Published by the American Astronomical Society. 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: 0004-637X ;EISSN: 1538-4357 ;DOI: 10.3847/1538-4357/ad276c

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