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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost
Luís Ferreira, André Pilastri, Carlos Martins, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2021), pp. 1-8
Open Access | Times Cited: 91

Showing 26-50 of 91 citing articles:

Utilization of Machine Learning Algorithms for the Strengthening of HIV Testing: A Systematic Review
Musa Jaiteh, Edith Phalane, Yegnanew A. Shiferaw, et al.
Algorithms (2024) Vol. 17, Iss. 8, pp. 362-362
Open Access | Times Cited: 3
Prediction of rural domestic water and sewage production based on automated machine learning in northern China
Yingnan Cao, Zixuan Wang, Pengyu Li, et al.
Journal of Cleaner Production (2023) Vol. 434, pp. 140016-140016
Closed Access | Times Cited: 8
Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River
Mikhail Sarafanov, Yulia Borisova, Mikhail Maslyaev, et al.
Water (2021) Vol. 13, Iss. 24, pp. 3482-3482
Open Access | Times Cited: 18
A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification
Marcelo V C Aragão, Augusto Guimarães Afonso, Rafaela Cristina Ferraz, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 2
A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics
Rui Ribeiro, André Pilastri, Carla Victoria Rodrigues de Moura, et al.
Neural Computing and Applications (2023) Vol. 35, Iss. 23, pp. 17375-17395
Open Access | Times Cited: 6
The Research of AutoML Methods in the Task of Wave Data Classification
Evgenii Neverov, Ilya I. Viksnin, Sergey Chuprov
(2023), pp. 156-158
Closed Access | Times Cited: 6
Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms
Emmanuel C. Opara, Hayden Wimmer, Carl Rebman
2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) (2022), pp. 1-10
Closed Access | Times Cited: 10
Predicting Yarn Breaks in Textile Fabrics: A Machine Learning Approach
João Azevedo, Rui Ribeiro, Luís Miguel Matos, et al.
Procedia Computer Science (2022) Vol. 207, pp. 2301-2310
Open Access | Times Cited: 9
A simulation of the impacts of machine learning to combine psychometric employee selection system predictors on performance prediction, adverse impact, and number of dropped predictors
Richard N. Landers, Elena M. Auer, Lily Dunk, et al.
Personnel Psychology (2023) Vol. 76, Iss. 4, pp. 1037-1060
Open Access | Times Cited: 5
Assessing the Use of AutoML for Data-Driven Software Engineering
Fabio Calefato, Luigi Quaranta, Filippo Lanubile, et al.
(2023), pp. 1-12
Open Access | Times Cited: 5
Real-time anomaly detection system within the scope of smart factories
Cihan Bayraktar, Ziya Karakaya, Hadı Gökċen
The Journal of Supercomputing (2023) Vol. 79, Iss. 13, pp. 14707-14742
Closed Access | Times Cited: 4
Lithology identification in carbonate thin section images of the Brazilian pre-salt reservoirs by the computational vision and deep learning
Elisângela L. Faria, Juliana Coelho, Thais F. Matos, et al.
Computational Geosciences (2022) Vol. 26, Iss. 6, pp. 1537-1547
Closed Access | Times Cited: 7
Prediction of Maintenance Equipment Failures Using Automated Machine Learning
Luís Ferreira, André Pilastri, Vítor C. Sousa, et al.
Lecture notes in computer science (2021), pp. 259-267
Closed Access | Times Cited: 9
TPOT-MTR: A Multiple Target Regression Based on Genetic Algorithm of Automated Machine Learning Systems
Hanafi Majid, Syahid Anuar, Noor Hafizah Hassan
Journal of Advanced Research in Applied Sciences and Engineering Technology (2023) Vol. 30, Iss. 3, pp. 104-126
Open Access | Times Cited: 3
Automated Prediction of Crack Propagation Using H2O AutoML
Intisar Omar, Muhammad Khan, Andrew Starr, et al.
Sensors (2023) Vol. 23, Iss. 20, pp. 8419-8419
Open Access | Times Cited: 3
Evolutionary Automated Machine Learning for Multi-Scale Decomposition and Forecasting of Sensor Time Series
Mikhail Sarafanov, Valerii Pokrovskii, Nikolay O. Nikitin
2022 IEEE Congress on Evolutionary Computation (CEC) (2022), pp. 01-08
Closed Access | Times Cited: 5
AutoFL: Towards AutoML in a Federated Learning Context
Davy Preuveneers
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8019-8019
Open Access | Times Cited: 2
A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires
Marcos Lacasa, Ferrán Prados, José Alegre, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 2
A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities
Daniela Soares, Pedro José Pereira, Paulo Cortez, et al.
Lecture notes in computer science (2023), pp. 311-322
Closed Access | Times Cited: 2
A Comparison of Automated Time Series Forecasting Tools for Smart Cities
Pedro José Pereira, Nuno Marques da Costa, Margarida Barros, et al.
Lecture notes in computer science (2022), pp. 551-562
Closed Access | Times Cited: 4
Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning
Afonso Sousa, Luís Ferreira, Rui Ribeiro, et al.
IFIP advances in information and communication technology (2022), pp. 262-273
Closed Access | Times Cited: 3
Optimizing Data Analytics Workflows through User-driven Experimentation
Keerthiga Rajenthiram
(2024), pp. 253-255
Open Access
Prediction of Total Anthocyanin Content in Single Kernel Maize Using Spectral and Color Space Data Coupled with Automl
Fatih Kahrıman, Umut Songur, Ezgi Alaca Yıldırım, et al.
(2024)
Closed Access
Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis
Tomasz Walczyna, Damian Jankowski, Zbigniew Piotrowski
Applied Sciences (2024) Vol. 15, Iss. 1, pp. 286-286
Open Access
Developing an Hourly Water Level Prediction Model for Small- and Medium-Sized Agricultural Reservoirs Using AutoML: Case Study of Baekhak Reservoir, South Korea
Jeongho Han, Joo Hyun Bae
Agriculture (2024) Vol. 15, Iss. 1, pp. 71-71
Open Access

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