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1
Nearest neighbors distance ratio open-set classifier
Nearest neighbors distance ratio open-set classifier
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Nearest neighbors distance ratio open-set classifier

Machine learning, 2017-03, Vol.106 (3), p.359-386 [Peer Reviewed Journal]

The Author(s) 2016 ;Machine Learning is a copyright of Springer, 2017. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-016-5610-8

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2
Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study
Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study
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Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study

Applied biochemistry and biotechnology, 2020-02, Vol.190 (2), p.341-359 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Applied Biochemistry and Biotechnology is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0273-2289 ;EISSN: 1559-0291 ;DOI: 10.1007/s12010-019-03093-z ;PMID: 31350666

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3
An effective combining classifier approach using tree algorithms for network intrusion detection
An effective combining classifier approach using tree algorithms for network intrusion detection
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An effective combining classifier approach using tree algorithms for network intrusion detection

Neural computing & applications, 2017-12, Vol.28 (Suppl 1), p.1051-1058 [Peer Reviewed Journal]

The Natural Computing Applications Forum 2016 ;Copyright Springer Science & Business Media 2017 ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-016-2418-1

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4
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
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The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Data mining and knowledge discovery, 2021-03, Vol.35 (2), p.401-449 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. ;The Author(s) 2020. 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: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-020-00727-3 ;PMID: 33679210

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5
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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6
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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7
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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8
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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9
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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10
Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations
Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations
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Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations

Data mining and knowledge discovery, 2019-07, Vol.33 (4), p.1183-1222 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Data Mining and Knowledge Discovery is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1384-5810 ;EISSN: 1573-756X ;DOI: 10.1007/s10618-019-00633-3

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11
Supervised learning with quantum-enhanced feature spaces
Supervised learning with quantum-enhanced feature spaces
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Supervised learning with quantum-enhanced feature spaces

Nature (London), 2019-03, Vol.567 (7747), p.209-212 [Peer Reviewed Journal]

COPYRIGHT 2019 Nature Publishing Group ;Copyright Nature Publishing Group Mar 14, 2019 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-019-0980-2 ;PMID: 30867609

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12
Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier
Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier
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Accuracy Improvement of Transformer Faults Diagnostic Based on DGA Data Using SVM-BA Classifier

Energies (Basel), 2021-05, Vol.14 (10), p.2970 [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. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14102970

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13
Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning
Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning
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Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning

Expert systems with applications, 2021-04 (167) [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0957-4174 ;EISSN: 1873-6793 ;DOI: 10.1016/j.eswa.2020.113855

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14
Swarm Learning for decentralized and confidential clinical machine learning
Swarm Learning for decentralized and confidential clinical machine learning
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Swarm Learning for decentralized and confidential clinical machine learning

Nature (London), 2021-06, Vol.594 (7862), p.265-270 [Peer Reviewed Journal]

COPYRIGHT 2021 Nature Publishing Group ;Copyright Nature Publishing Group Jun 10, 2021 ;The Author(s) 2021 ;ISSN: 0028-0836 ;EISSN: 1476-4687 ;DOI: 10.1038/s41586-021-03583-3 ;PMID: 34040261

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15
Arabic text classification: the need for multi-labeling systems
Arabic text classification: the need for multi-labeling systems
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Arabic text classification: the need for multi-labeling systems

Neural computing & applications, 2022-01, Vol.34 (2), p.1135-1159 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-021-06390-z ;PMID: 34483495

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16
A review of epileptic seizure detection using machine learning classifiers
A review of epileptic seizure detection using machine learning classifiers
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A review of epileptic seizure detection using machine learning classifiers

Brain informatics, 2020-05, Vol.7 (1), p.5-5, Article 5 [Peer Reviewed Journal]

The Author(s) 2020 ;COPYRIGHT 2020 Springer ;The Author(s) 2020. 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: 2198-4018 ;EISSN: 2198-4026 ;DOI: 10.1186/s40708-020-00105-1 ;PMID: 32451639

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17
Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier
Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier
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Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier

Neural computing & applications, 2022-06, Vol.34 (12), p.9579-9592 [Peer Reviewed Journal]

Springer-Verlag London Ltd., part of Springer Nature 2020 ;Springer-Verlag London Ltd., part of Springer Nature 2020. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-020-04842-6

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18
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis
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Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Proceedings of the National Academy of Sciences - PNAS, 2020-06, Vol.117 (23), p.12592-12594 [Peer Reviewed Journal]

Copyright © 2020 the Author(s). Published by PNAS. ;Copyright National Academy of Sciences Jun 9, 2020 ;Copyright © 2020 the Author(s). Published by PNAS. 2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1919012117 ;PMID: 32457147

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19
A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks
A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks
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A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks

Sensors (Basel, Switzerland), 2019-10, Vol.19 (20), p.4383 [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: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s19204383 ;PMID: 31658774

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20
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Machine learning, 2021-03, Vol.110 (3), p.457-506 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. 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: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05946-3

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