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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:

Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential
Emrehan Kutluğ Şahin, Selçuk Demir
Engineering Applications of Artificial Intelligence (2022) Vol. 119, pp. 105732-105732
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

AutoML: A systematic review on automated machine learning with neural architecture search
Imrus Salehin, Md. Shamiul Islam, Pritom Saha, et al.
Journal of Information and Intelligence (2023) Vol. 2, Iss. 1, pp. 52-81
Open Access | Times Cited: 42
Evaluation and analysis of liquefaction potential of gravelly soils using explainable probabilistic machine learning model
Kaushik Jas, Sujith Mangalathu, G. R. Dodagoudar
Computers and Geotechnics (2024) Vol. 167, pp. 106051-106051
Closed Access | Times Cited: 19
Impact assessment of geohazards triggered by 6 February 2023 Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6) on the natural gas pipelines
Erdinç Örsan Ünal, Sultan Kocaman, Candan Gökçeoğlu
Engineering Geology (2024) Vol. 334, pp. 107508-107508
Closed Access | Times Cited: 16
AutoML-GWL: Automated machine learning model for the prediction of groundwater level
Abhilash Singh, Sharad Patel, Vipul Bhadani, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107405-107405
Closed Access | Times Cited: 27
A Scientometrics Review of Soil Properties Prediction Using Soft Computing Approaches
Jitendra Khatti, Kamaldeep Singh Grover
Archives of Computational Methods in Engineering (2023) Vol. 31, Iss. 3, pp. 1519-1553
Closed Access | Times Cited: 22
Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames
Ahmet Demir, Emrehan Kutluğ Şahin, Selçuk Demir
Structures (2024) Vol. 64, pp. 106524-106524
Closed Access | Times Cited: 7
SWSEL: Sliding Window-based Selective Ensemble Learning for class-imbalance problems
Qi Dai, Jian‐wei Liu, Jiapeng Yang
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 105959-105959
Closed Access | Times Cited: 19
Data‐Driven Tunnel Oxide Passivated Contact Solar Cell Performance Analysis Using Machine Learning
Jiakai Zhou, T. Jesper Jacobsson, Zhi Wang, et al.
Advanced Materials (2024) Vol. 36, Iss. 14
Closed Access | Times Cited: 6
Cone penetration test-based assessment of liquefaction potential using machine and hybrid learning approaches
Jitendra Khatti, Yewuhalashet Fissha, Kamaldeep Singh Grover, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3841-3864
Closed Access | Times Cited: 5
Landslide susceptibility assessment using AutoML-SHAP method in the southern foothills of Changbai Mountain, China
Defeng Zheng, Yuanyuan Li, Chenglin Yan, et al.
Landslides (2025)
Closed Access
A comprehensive evaluation of ensemble machine learning in geotechnical stability analysis and explainability
Shan Lin, Zenglong Liang, Shuaixing Zhao, et al.
International Journal of Mechanics and Materials in Design (2023) Vol. 20, Iss. 2, pp. 331-352
Closed Access | Times Cited: 15
Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches
Eyyüp Hakan Şehmusoğlu, Талас Фикрет Курназ, Caner Erden
Environmental Earth Sciences (2025) Vol. 84, Iss. 5
Open Access
The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction
Selçuk Demir, Emrehan Kutluğ Şahin
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 8, pp. 3273-3290
Open Access | Times Cited: 3
Spatio-temporal visual learning for home-based monitoring
Youcef Djenouri, Ahmed Nabil Belbachir, Alberto Cano, et al.
Information Fusion (2023) Vol. 101, pp. 101984-101984
Open Access | Times Cited: 9
Probabilistic-based seismic fragility assessment of earthquake-induced site liquefaction
Jian-Yu Meng, Dagang Lü, Baohua Shan
Soil Dynamics and Earthquake Engineering (2023) Vol. 175, pp. 108250-108250
Closed Access | Times Cited: 7
Proposing Machine Learning Models Suitable for Predicting Open Data Utilization
Junyoung Jeong, K. Cho
(2024)
Open Access | Times Cited: 1
Proposing Machine Learning Models Suitable for Predicting Open Data Utilization
Junyoung Jeong, K. Cho
Sustainability (2024) Vol. 16, Iss. 14, pp. 5880-5880
Open Access | Times Cited: 1
Enhancing soil liquefaction risk assessment with metaheuristics and hybrid learning techniques
Jui‐Sheng Chou, Tran-Bao-Quyen Pham
Georisk Assessment and Management of Risk for Engineered Systems and Geohazards (2024), pp. 1-19
Closed Access | Times Cited: 1
Leveraging Bayesian methods for addressing multi-uncertainty in data-driven seismic liquefaction assessment
Zhihui Wang, Roberto Cudmani, Andrés Alfonso Peña Olarte, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 1
Automl-Based Predictive Maintenance Model for Accurate Failure Detection
Elif Cesur, Muhammet Raşit Cesur, Şeyma Duymaz
Lecture notes in mechanical engineering (2023), pp. 641-650
Closed Access | Times Cited: 3
Integrating Artificial Intelligence Techniques for Enhanced Smart Control Systems: A Framework Utilizing Ensemble Weighted Extreme Learning Machine
Javad Palizvan Zand, Javad Katebi, Afshin Bahrami Rad, et al.
(2024)
Closed Access
Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction
Rajarshi Bhattacharjee, Shishir Gaur, Shard Chander, et al.
Environmental Science and Pollution Research (2024)
Closed Access
FastLSM-AutoML: Fast, reliable, and robust end-to-end AutoML tool for producing a landslide susceptibility map
Emrehan Kutluğ Şahin
Stochastic Environmental Research and Risk Assessment (2024)
Closed Access
An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion
Mingyuan Wang, Shaoxiang Zeng, Z. Zhang, et al.
Advances in Civil Engineering (2024) Vol. 2024, Iss. 1
Open Access
Deep ensemble learning for high-dimensional subsurface fluid flow modeling
Abouzar Choubineh, Jie Chen, David A. Wood, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106968-106968
Open Access | Times Cited: 1
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