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21
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System
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TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System

IEEE access, 2019, Vol.7, p.94497-94507 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2928048 ;CODEN: IAECCG

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22
EEG-Based Emotion Classification Using Stacking Ensemble Approach
EEG-Based Emotion Classification Using Stacking Ensemble Approach
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EEG-Based Emotion Classification Using Stacking Ensemble Approach

Sensors (Basel, Switzerland), 2022-11, Vol.22 (21), p.8550 [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/s22218550 ;PMID: 36366249

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23
EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers
EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers
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EEG Authentication System Based on One- and Multi-Class Machine Learning Classifiers

Sensors (Basel, Switzerland), 2022-12, Vol.23 (1), p.186 [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/s23010186 ;PMID: 36616785

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24
A Random Forest classifier-based approach in the detection of abnormalities in the retina
A Random Forest classifier-based approach in the detection of abnormalities in the retina
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A Random Forest classifier-based approach in the detection of abnormalities in the retina

Medical & biological engineering & computing, 2019, Vol.57 (1), p.193-203 [Peer Reviewed Journal]

International Federation for Medical and Biological Engineering 2018 ;Medical & Biological Engineering & Computing is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-018-1878-0 ;PMID: 30076537

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25
Tweets Classification on the Base of Sentiments for US Airline Companies
Tweets Classification on the Base of Sentiments for US Airline Companies
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Tweets Classification on the Base of Sentiments for US Airline Companies

Entropy (Basel, Switzerland), 2019-11, Vol.21 (11), p.1078 [Peer Reviewed Journal]

2019 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. ;2019 by the authors. 2019 ;ISSN: 1099-4300 ;EISSN: 1099-4300 ;DOI: 10.3390/e21111078

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26
Digital twin design of pneumatic classifier and its key technology
Digital twin design of pneumatic classifier and its key technology
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Digital twin design of pneumatic classifier and its key technology

Journal of physics. Conference series, 2023-09, Vol.2589 (1), p.12028 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2589/1/012028

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27
A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park's Vector Components of Current and Voltage
A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park's Vector Components of Current and Voltage
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A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park's Vector Components of Current and Voltage

Sensors (Basel, Switzerland), 2021-11, Vol.21 (23), p.7900 [Peer Reviewed Journal]

2021 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. ;2021 by the authors. 2021 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21237900 ;PMID: 34883902

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28
Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation
Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation
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Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation

Computers in biology and medicine, 2024-05, Vol.173, p.108345-108345, Article 108345 [Peer Reviewed Journal]

2024 Elsevier Ltd ;Copyright © 2024 Elsevier Ltd. All rights reserved. ;2024. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2024.108345 ;PMID: 38564852

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29
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
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Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Computers in biology and medicine, 2020-08, Vol.123, p.103899, Article 103899 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103899 ;PMID: 32768046

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30
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification
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nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification

BMC bioinformatics, 2014-09, Vol.15 (1), p.298-298, Article 298 [Peer Reviewed Journal]

COPYRIGHT 2014 BioMed Central Ltd. ;2014 Song 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. ;Song et al.; licensee BioMed Central Ltd. 2014 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/1471-2105-15-298 ;PMID: 25196432

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31
Types of minority class examples and their influence on learning classifiers from imbalanced data
Types of minority class examples and their influence on learning classifiers from imbalanced data
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Types of minority class examples and their influence on learning classifiers from imbalanced data

Journal of intelligent information systems, 2016-06, Vol.46 (3), p.563-597 [Peer Reviewed Journal]

The Author(s) 2015 ;Springer Science+Business Media New York 2016 ;ISSN: 0925-9902 ;EISSN: 1573-7675 ;DOI: 10.1007/s10844-015-0368-1

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32
Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews
Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews
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Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews

Journal of clinical epidemiology, 2021-05, Vol.133, p.140-151 [Peer Reviewed Journal]

2020 The Authors ;Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved. ;2020. The Authors ;2020 The Authors 2020 ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2020.11.003 ;PMID: 33171275

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33
Prevalence of neural collapse during the terminal phase of deep learning training
Prevalence of neural collapse during the terminal phase of deep learning training
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Prevalence of neural collapse during the terminal phase of deep learning training

Proceedings of the National Academy of Sciences - PNAS, 2020-10, Vol.117 (40), p.24652-24663 [Peer Reviewed Journal]

Copyright National Academy of Sciences Oct 6, 2020 ;Copyright © 2020 the Author(s). Published by PNAS. 2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2015509117 ;PMID: 32958680

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34
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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35
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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36
Online Active Learning Ensemble Framework for Drifted Data Streams
Online Active Learning Ensemble Framework for Drifted Data Streams
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Online Active Learning Ensemble Framework for Drifted Data Streams

IEEE transaction on neural networks and learning systems, 2019-02, Vol.30 (2), p.486-498

ISSN: 2162-237X ;EISSN: 2162-2388 ;DOI: 10.1109/TNNLS.2018.2844332 ;CODEN: ITNNAL

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37
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
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Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

Remote sensing (Basel, Switzerland), 2015-01, Vol.7 (1), p.153-168 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs70100153

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38
Binary search of the optimal cut-point value in ROC analysis using the F1 score
Binary search of the optimal cut-point value in ROC analysis using the F1 score
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Binary search of the optimal cut-point value in ROC analysis using the F1 score

Journal of physics. Conference series, 2023-10, Vol.2609 (1), p.012002 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2609/1/012002

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39
Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM
Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM
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Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

Applied intelligence (Dordrecht, Netherlands), 2021-10, Vol.51 (10), p.7094-7108 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. ;ISSN: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-021-02205-9

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40
A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction
A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction
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A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction

Annals of operations research, 2021-01, Vol.296 (1-2), p.495-512 [Peer Reviewed Journal]

The Author(s) 2019 ;COPYRIGHT 2021 Springer ;Annals of Operations Research is a copyright of Springer, (2019). All Rights Reserved. © 2019. 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) 2019. This work is published 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. ;ISSN: 0254-5330 ;EISSN: 1572-9338 ;DOI: 10.1007/s10479-019-03223-0

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