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1
An efficient method for bearing fault diagnosis
An efficient method for bearing fault diagnosis
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An efficient method for bearing fault diagnosis

Systems science & control engineering, 2024-12, Vol.12 (1) [Peer Reviewed Journal]

EISSN: 2164-2583 ;DOI: 10.1080/21642583.2024.2329264

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2
A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques
A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques
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A multimodal fusion framework to diagnose cotton leaf curl virus using machine vision techniques

Cogent food & agriculture, 2024-12, Vol.10 (1) [Peer Reviewed Journal]

EISSN: 2331-1932 ;DOI: 10.1080/23311932.2024.2339572

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3
Smart polarization and spectroscopic holography for real-time microplastics identification
Smart polarization and spectroscopic holography for real-time microplastics identification
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Smart polarization and spectroscopic holography for real-time microplastics identification

Communications engineering, 2024-12, Vol.3 (1), p.32-8 [Peer Reviewed Journal]

The Author(s) 2024. 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) 2024 ;EISSN: 2731-3395 ;DOI: 10.1038/s44172-024-00178-4

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4
A proposed hybrid framework to improve the accuracy of customer churn prediction in telecom industry
A proposed hybrid framework to improve the accuracy of customer churn prediction in telecom industry
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A proposed hybrid framework to improve the accuracy of customer churn prediction in telecom industry

Journal of big data, 2024-12, Vol.11 (1), p.70-27 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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. ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-024-00922-9

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5
Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network
Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network
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Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network

Journal of engineering and applied science (Online), 2024-12, Vol.71 (1), p.26-18, Article 26

The Author(s) 2024 ;The Author(s) 2024. 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: 1110-1903 ;EISSN: 2536-9512 ;DOI: 10.1186/s44147-023-00335-0

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6
IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis
IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis
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IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis

Social network analysis and mining, 2024-12, Vol.14 (1), p.89 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 1869-5450 ;EISSN: 1869-5469 ;DOI: 10.1007/s13278-024-01249-2

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7
Online learners’ engagement detection via facial emotion recognition in online learning context using hybrid classification model
Online learners’ engagement detection via facial emotion recognition in online learning context using hybrid classification model
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Online learners’ engagement detection via facial emotion recognition in online learning context using hybrid classification model

Social network analysis and mining, 2024-12, Vol.14 (1), p.43 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 1869-5450 ;EISSN: 1869-5469 ;DOI: 10.1007/s13278-023-01181-x

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8
Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning
Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning
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Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning

EURASIP journal on audio, speech, and music processing, 2024-12, Vol.2024 (1), p.14-13 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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: 1687-4714 ;EISSN: 1687-4722 ;DOI: 10.1186/s13636-024-00336-8

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9
Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening
Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening
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Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening

Health information science and systems, 2024-12, Vol.12 (1), p.18-18 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;COPYRIGHT 2024 BioMed Central Ltd. ;ISSN: 2047-2501 ;EISSN: 2047-2501 ;DOI: 10.1007/s13755-024-00277-8 ;PMID: 38464462

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10
A deep ensemble learning framework for COVID-19 detection in chest X-ray images
A deep ensemble learning framework for COVID-19 detection in chest X-ray images
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A deep ensemble learning framework for COVID-19 detection in chest X-ray images

Network modeling and analysis in health informatics and bioinformatics (Wien), 2024-12, Vol.13 (1), p.30 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 2192-6662 ;EISSN: 2192-6670 ;DOI: 10.1007/s13721-024-00466-1

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11
A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data
A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data
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A turbulent flow optimized deep fused ensemble model (TFO-DFE) for sentiment analysis using social corpus data

Social network analysis and mining, 2024-12, Vol.14 (1), p.41 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 1869-5450 ;EISSN: 1869-5469 ;DOI: 10.1007/s13278-024-01203-2

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12
The role of classifiers and data complexity in learned Bloom filters: insights and recommendations
The role of classifiers and data complexity in learned Bloom filters: insights and recommendations
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The role of classifiers and data complexity in learned Bloom filters: insights and recommendations

Journal of big data, 2024-12, Vol.11 (1), p.45-26 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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. ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-024-00906-9

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13
Business text classification with imbalanced data and moderately large label spaces for digital transformation
Business text classification with imbalanced data and moderately large label spaces for digital transformation
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Business text classification with imbalanced data and moderately large label spaces for digital transformation

Applied network science, 2024-12, Vol.9 (1), p.11-12 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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. ;EISSN: 2364-8228 ;DOI: 10.1007/s41109-024-00623-5

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14
A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data
A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data
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A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data

npj computational materials, 2024-12, Vol.10 (1), p.54-10 [Peer Reviewed Journal]

The Author(s) 2024. 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. ;EISSN: 2057-3960 ;DOI: 10.1038/s41524-024-01241-6

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15
Predicting game-induced emotions using EEG, data mining and machine learning
Predicting game-induced emotions using EEG, data mining and machine learning
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Predicting game-induced emotions using EEG, data mining and machine learning

Bulletin of the National Research Centre, 2024-12, Vol.48 (1), p.57-10 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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. ;EISSN: 2522-8307 ;DOI: 10.1186/s42269-024-01200-7

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16
Landslide susceptibility assessment of South Korea using stacking ensemble machine learning
Landslide susceptibility assessment of South Korea using stacking ensemble machine learning
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Landslide susceptibility assessment of South Korea using stacking ensemble machine learning

Geoenvironmental disasters, 2024-12, Vol.11 (1), p.7-17 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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. ;EISSN: 2197-8670 ;DOI: 10.1186/s40677-024-00271-y

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17
Deep facial expression detection using Viola-Jones algorithm, CNN-MLP and CNN-SVM
Deep facial expression detection using Viola-Jones algorithm, CNN-MLP and CNN-SVM
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Deep facial expression detection using Viola-Jones algorithm, CNN-MLP and CNN-SVM

Social network analysis and mining, 2024-12, Vol.14 (1), p.65 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;ISSN: 1869-5450 ;EISSN: 1869-5469 ;DOI: 10.1007/s13278-024-01231-y

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18
Beyond the use of a novel Ensemble based Random Forest-BERT Model (Ens-RF-BERT) for the Sentiment Analysis of the hashtag COVID19 tweets
Beyond the use of a novel Ensemble based Random Forest-BERT Model (Ens-RF-BERT) for the Sentiment Analysis of the hashtag COVID19 tweets
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Article 		Add to My Research

Beyond the use of a novel Ensemble based Random Forest-BERT Model (Ens-RF-BERT) for the Sentiment Analysis of the hashtag COVID19 tweets

Social network analysis and mining, 2024-12, Vol.14 (1), p.88 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1869-5450 ;EISSN: 1869-5469 ;DOI: 10.1007/s13278-024-01240-x

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19
Innovative deep learning techniques for monitoring aggressive behavior in social media posts
Innovative deep learning techniques for monitoring aggressive behavior in social media posts
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Innovative deep learning techniques for monitoring aggressive behavior in social media posts

Journal of cloud computing : advances, systems and applications, 2024-12, Vol.13 (1), p.19-13, Article 19 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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: 2192-113X ;EISSN: 2192-113X ;DOI: 10.1186/s13677-023-00577-6

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20
Synthesizing class labels for highly imbalanced credit card fraud detection data
Synthesizing class labels for highly imbalanced credit card fraud detection data
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Synthesizing class labels for highly imbalanced credit card fraud detection data

Journal of big data, 2024-12, Vol.11 (1), p.38-22 [Peer Reviewed Journal]

The Author(s) 2024 ;The Author(s) 2024. 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. ;EISSN: 2196-1115 ;DOI: 10.1186/s40537-024-00897-7

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